Publications

Book

  1. Tóth, R.: Modeling and identification of linear parameter-varying systems. Lecture Notes in Control and Information Sciences, Vol. 403, Springer, Heidelberg, 2010. (book webpage) (link)

Book Chapters (5)

  1. Piga, D., S. Formentin, R. Tóth, A. Bemporad and S.M. Savaresi: A hierarchical approach to data-driven LPV control design of constrained systems. In C. Novara and S. Formentin (Eds): Data-Driven Modeling, Filtering and Control: Methods and Applications (pp. 213-237), The Institution of Engineering and Technology, 2019. (link)
  2. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof:  Prediction error identification of LPV systems: present and beyond. In: J. Mohammadpour and C. W. Scherer (Eds.), Control of Linear Parameter Varying Systems with Applications (pp. 27-60), Springer, Heidelberg, 2012. (pdf) (link)
  3. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof:  LPV system identification using series-expansion models. In: P. L. dos Santos, C. Novara, D. Rivera, J. Ramos and T-P. Perdicoúlis (Eds.), Linear Parameter-Varying System Identification: New Developments and Trends (pp. 259-294), World Scientific Publishing, Singapore, 2011. (pdf) (link)
  4. Laurain, V., M. Gilson, H. Garnier, R. Tóth:  Identification of discrete-time and continuous-time input/output LPV models. In: P. L. dos Santos, C. Novara, D. Rivera, J. Ramos and T-P. Perdicoúlis (Eds.), Linear Parameter-Varying System Identification: New Developments and Trends (pp. 95-132), World Scientific Publishing, Singapore, 2011. (pdf) (link)
  5. Van den Hof, P. M. J., R. Tóth and P. S. C. Heuberger: Model structures for identification of linear parameter-varying (LPV) models, In: K. M. Hangos and L. Nádai (Ed.). Proceedings of the Workshop on Systems and Control Theory in honor of József Bokor on his 60th Birthday (pp. 15-34), MTA, Budapest, 2009. (pdf)

Journal Papers (63)

  1. Beintema, G. I., M. Schoukens, and R. Tóth: Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems. Accepted to Automatica (2024). (ELKH) (pdf is available upon request).
  2. Wang R., R. Tóth and I. R. Manchester: Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding. In print, International Journal of Robust and Nonlinear Control (2023). (ERC, ARNL) (pdf is available upon request)
  3. Retzler, A., R. Tóth, M. Schoukens , G. I. Beintema, J. Weigand, J.-P. Noël, Zs. Kollár, and J. Swevers: Learning based augmentation of physics-based models: an industrial robot use case, In print, Data-Centric Engineering (2024).
  4. Bloemers, T.A.H.,  S. Leemrijse, V. Preda, F. Boquet, T.A.E. Oomen, and R. Tóth: Vibration Control under Frequency-Varying Disturbances with Application to Satellites. In print, IEEE Transactions on Control Systems Technology (2023). (OSIP, ARNL) 
  5. Verhoek, C., J. Berberich, S. Haesaert, F. Allgöwer, and R. Tóth: Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems, In print, IEEE Transactions on Automatic Control (2024). (ERC, ARNL) (pdf is available upon request)
  6. Liu, Y., P. Wang, C.-H. Lee, and R. Tóth: Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning, IEEE Transactions on Aerospace and Electronic Systems, In print, (2024).
  7. Iacob, L. C., R Tóth and M. Schoukens: Koopman Form of Nonlinear Systems with Inputs, Automatica, Volume 162, pp. 111525, (2024). (ERC, ELKH)
  8. Ignéczi, G. F., E. Horváth, R. Tóth, and K. Nyilas: Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles, Automotive Innovation, (2024). (JKK, ELKH, ARNL)
  9. Antal, P., T. Peni and R. Tóth: Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning. IEEE Transactions on Control Systems Technology, Volume: 32, Issue: 1, pp. 3-14, (2024). (ELKH)
  10. Antal, P., T. Péni, and R. Tóth: Modelling, identification and geometric control of autonomous quadcopters for agile maneuvering. Aeronautical Science Bulletins, Vol. 35, pp. 141-160, (2023).  (JKK, ELKH, ARNL) 
  11. Beintema, G. I., M. Schoukens, and R. Tóth: Deep Subspace Encoders for Nonlinear System Identification. Automatica, Vol. 156, pp. 111210, (2023). (ELKH)
  12. Petreczky, M., R. Tóth, and G. Mercère: Minimal realizations of input-output behaviors by LPV state-space representations with affine dependency,  IEEE Control Systems Letters, Vol 7., pp. 2952-2957, (2023). (ARNL)
  13. Broens, Y., H. Butler and R. Tóth: On improved commutation for moving-magnet planar actuators, IEEE Control Systems Letters, In print, (2023). (IT2, ARNL) 
  14. Polcz, P., T. Péni, and R. Tóth: Efficient implementation of Gaussian Process Based Predictive Control by Quadratic Programming, IET Control Theory & Applications, Vol. 17, Issue: 8, pp. 943-1087, (2023). (ELKH, ARNL)
  15. Khandelwal, D., M. Schoukens, and R. Tóth: Automated Multi-Objective System Identification Using Grammar-Based Genetic Programming, Automatica, Vol. 154, pp. 111017 (2023). (CADUSY, ELKH)
  16. Petreczky, M., R. Tóth, and G. Mercère: LPV-ARX Representations of LPV State-Space Models with Affine Dependence, Systems and Control Letters, Vol. 173, pp. 105459, (2023). (ELKH, ERC) 
  17. Verhoek, C. , P.J.W. Koelewijn, S. Haesaert and R. Tóth: Convex Incremental Dissipativity Analysis of Nonlinear Systems. Automatica, Vol. 150, pp. 110859, (2023). (ERC, ARNL)
  18. Sadeghzadeh, A. and R. Tóth: Improved Embedding of Nonlinear Systems in Linear Parameter-Varying Models with Polynomial Dependence,  IEEE Transactions on Control Systems Technology, Vol. 31, Issue 1, pp. 70-82, (2022). (ERC, ARNL) 
  19. Bloemers, T.A.H.,  T.A.E. Oomen, R. Tóth: Frequency Response Data Based LPV Controller Synthesis Applied to a Control Moment Gyroscope, IEEE Transactions on Control Systems Technology, Vol. 30, Issue 6, pp. 2734-2742, (2022). (ERC, ARNL) 
  20. Proimadis, I., C.H.H.M. Custers, J.W. Jansen, H. Butler, R. Tóth, E.A. Lomonova and P.M.J. Van den Hof: Active deformation control for a magnetically-levitated planar motor mover, IEEE Transactions on Industry Applications, Vol. 58, Issue 1, pp. 242-249, (2022). (NAPAS, ARNL) 
  21. Bloemers, T.A.H., T.A.E. Oomen and R. Tóth: Frequency Response Data-driven LPV Controller Synthesis for MIMO Systems, IEEE Control Systems Letters, Vol. 6, pp. 2264 – 2269, (2022). (ERC, ARNL)
  22. Abbas, H. S., R. Tóth, M. Petreczky, N. Meskin, J. Mohammadpour and P.J.W. Koelewijn: LPV Modeling of Nonlinear Systems: A Multi-Path Feedback Linearisation Approach, International Journal of Robust and Nonlinear Control, Vol. 31, Issue 18, pp. 9436-9465, (2021). (ERC)
  23. Wang R., P.J.W. Koelewijn, I. R. Manchester and R. Tóth: Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics, special issue, Journal of Robust and Nonlinear Control, Vol. 31, Issue 17, pp. 8147-8164, (2021). (ERC)
  24. Hanema, J., R. Tóth and M. Lazar: Stabilizing non-linear model predictive control using linear parameter-varying embeddings and tubes. IET Control Theory & Applications, (2021), pp. 1-18. (ERC)
  25. Cox, P. B., and R. Tóth: Linear Parameter-Varying Subspace Identification: A Unified Framework. Automatica, Vol. 123, pp. 109296 (2021).  (ERC) 
  26. Sadeghzadeh, A., B. Sharif and R. Tóth: Affine linear parameter-varying embedding of non-linear models with improved accuracy and minimal overbounding, IET Control Theory & Applications, Volume 14, Issue 20, (2020) pp. 3363 – 3373. (ERC) 
  27. Khandelwal, D., M. Schoukens and R. Tóth: A Tree Adjoining Grammar representation for models of stochastic dynamical systems, Vol. 119, Automatica, (2020).
  28. Laurain, V., R. Tóth, D. Piga, M.A.H. Darwish: Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification, Automatica, Vol. 115, (2020). (ERC)
  29. Hanema, J., M. Lazar and R. Tóth: Heterogeneously parameterized tube model predictive control for LPV systems, Automatica, Vol. 111, (2020). (ERC) (pdf) (link)
  30. Darwish , M. A. H., P. B. Cox, I. Proimadis, G. Pillonetto and R. Tóth:  Prediction-Error Identification of LPV Systems: A Nonparametric Gaussian Regression Approach, Automatica, Vol. 97, (2018), pp. 92-103. (pdf) (link)
  31. Cox, P. B., R. Tóth, M. Petreczky: Towards Efficient Maximum Likelihood Estimation of LPV-SS Models, Automatica, Vol. 97, (2018), pp 392-403. (ERC) (pdf) (data) (link) (arXiv)
  32. Cox, P. B., S. Weiland and R. Tóth: Affine Parameter-Dependent Lyapunov Functions for LPV Systems with Affine Dependence, IEEE Transactions on Automatic Control, Vol. 63, No. 11, (2018), pp. 3865-3872. (ERC) (pdf) (link) (arxiv)
  33. Rizvi, S.Z., J. Mohammadpour, F. Abbasi, R. Tóth and  M. Meskin: State-space LPV Model Identification Using Kernelized Machine Learning, Automatica, Vol. 88, (2018), pp. 38-47. (pdf) (link)
  34. Abbas, H. S., J. Hanema, R. Tóth, N. Meskin, and J. Mohammadpour: An Improved Robust Model Predictive Control for Linear Parameter-Varying Input-Output Models, International Journal of Robust and Nonlinear Control, Vol. 28, No. 3, (2018), pp. 859-880. (pdf) (link)
  35. Darwish , M. A. H., G. Pillonetto and R. Tóth: The Quest for the Right Kernel in Bayesian Impulse Response Identification: The Use of OBFs, Automatica, Vol. 87, (2018), pp. 318-329. (pdf) (link)
  36. Golabi, A., N. Meskin, R. Tóth, J. Mohammadpour and T. Donkers: Event-triggered Reference Tracking Control for Discrete-time LPV Systems with Application to a Laboratory Tank System, IET Control Theory & Applications, Vol. 11, No. 16, (2017), pp. 2680-2687. (pdf) (link)
  37. Hanema, J., M. Lazar and R. Tóth: Stabilizing Tube-Based Model Predictive Control for LPV Systems,  Automatica, Vol. 85, (2017), pp. 137–144. (pdf) (link)
  38. Wollnack, S., H. S. Abbas, H. Werner, and R. Tóth: Fixed-Structure LPV-IO Controllers: An Implicit Representation Based Approach, Automatica, Vol. 83. (2017), pp 282–289. (pdf) (link)
  39. Chitraganti, S., R. Tóth, N. Meskin and J. Mohammadpour: Stochastic model predictive tracking of piecewise constant references for LPV systems, IET Control Theory & Applications, Vol. 11, No. 12, (2017), pp 1862-1872. (pdf) (link)
  40. Golabi, A., N. Meskin, R. Tóth  and J. Mohammadpour: A Bayesian Approach for LPV Model Identification and its Application to Complex Chemical Processes, IEEE Transactions on Control Systems Technology, Vol. 25, No. 6, (2017), pp. 2160-2167. (pdf) (link)
  41. Petreczky, M., R. Tóth and G. Mercere: Realization Theory for LPV State-Space Representations with Affine Dependence, IEEE Transactions on Automatic Control, Vol. 62, No. 9, (2017), pp. 4667-4674. (pdf) (link)
  42. Lataire, J., R. Pintelon, D. Piga, and R. Tóth: Continuous-Time Linear Time-Varying System Identification with a Frequency-Domain Kernel Based Estimator, IET Control Theory & Applications, Vol. 11, No. 4, (2017), pp. 457-465. (pdf) (link)
  43. Abbas, H. S., R. Tóth, N. Meskin, J. Mohammadpour, J. Hanema: A Robust MPC for Input-Output LPV Models, IEEE Transactions on Automatic Control, Vol. 61, No. 12, (2016), pp. 4183-4188. (pdf) (link)
  44. Rahme, S., H. S. Abbas, N. Meskin, R. Tóth  and J. Mohammadpour: LPV Model Development and Control of A Solution Copolymerization Reactor, Control Engineering Practice, Vol. 48, (2016), pp. 98-110. (pdf) (link)
  45. Rizvi, S.Z., J. Mohammadpour, R. Tóth and N. Meskin: A Kernel-based PCA Approach to Model Reduction of Linear Parameter-varying Systems, IEEE Transactions on Control Systems Technology, Vol. 24, No. 5, (2016), pp. 1883-1891. (pdf) (link)
  46. Formentin, S., D. Piga, R. Tóth, S. Savaresi: Direct learning of LPV controllers from data, Automatica, Vol. 65, (2016), pp. 98-110. (pdf) (link)
  47. Verbert, K. A. J., R. Tóth and R. Babuška: Adaptive Friction Compensation: A Globally Stable Approach, IEEE/ASME Transactions on Mechatronics, Vol. 21, No. 1, (2016), pp. 351-363. (pdf) (link)
  48. Laurain, V., R. Tóth, D. Piga, W. X. Zheng: An Instrumental Least Squares Support Vector Machine for Nonlinear System Identification, Automatica, Vol. 54, (2015), pp 340-347. (pdf) (link)
  49. Piga, D., P. Cox, R. Tóth and V. Laurain: LPV system identification under noise corrupted scheduling and output signal observations, Automatica , Vol. 53, (2015), pp. 329-338. (pdf) (link)
  50. Rojas, C. R., R. Tóth and H. Hjalmarsson: Sparse estimation of polynomial and rational dynamic models, special issue, IEEE Transactions on Automatic Control, Vol. 59, No. 11, (2014), pp. 2962-2977. (pdf) (link)
  51. Piga, D. and R. Tóth: A bias-corrected estimator for nonlinear systems with output-error type model structures, Automatica, Vol. 50, No. 9, (2014), pp. 2373-2380. (pdf) (link)
  52. Bachnas, A. A., R. Tóth, A. Mesbah, J. H. A. Ludlage: A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study, Journal of Process Control, Vol. 24, No. 4, (2014), pp. 272–285. (pdf) (errata) (link)
  53. Piga, D. and R. Tóth: An SDP approach for l_0-minimization: application to ARX model segmentation, Automatica, Vol. 49, No. 12, (2013), pp. 3646–3653. (pdf) (link)
  54. Tóth, R., V. Laurain, M. Gilson and H. Garnier: Instrumental variable scheme for closed-loop LPV model identification, Automatica, Vol. 48, No. 9, (2012), pp. 2314-2320. (pdf) (link)
  55. Tóth, R., M. Lovera, P. S. C. Heuberger, M. Corno and P. M. J. Van den Hof: On the discretization of linear fractional representations of LPV systems, IEEE Transactions on Control Systems Technology, Vol. 20, No. 6, (2012), pp. 1473-1489. (pdf) (link) (tech report)
  56. Tóth, R., H. Abbas and H. Werner: On the state-space realization of LPV input-output models: practical approaches, IEEE Transactions on Control Systems Technology, Vol. 20, No. 1, (2012), pp. 139-153. (pdf) (errata) (link)
  57. Laurain, V., R. Tóth, M. Gilson and H. Garnier: Direct identification of continuous-time LPV input/output models, special issue, IET Control Theory & Applications, Vol. 5, No. 7, (2011), pp. 878-888. (pdf) (link)
  58. Tóth, R., J. C. Willems, P. S. C. Heuberger and P. M. J. Van den Hof: The behavioral approach to linear parameter-varying systems, IEEE Transactions on Automatic Control, Vol. 56, No. 11, (2011), pp. 2499-2514. (pdf) (link)
  59. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: Discretisation of linear parameter-varying state-space representations, IET Control Theory & Applications, Vol. 4, No. 10, (2010), pp. 2082-2096. (pdf) (link)
  60. Laurain, V., M. Gilson, R. Tóth and H. Garnier: Refined instrumental variable methods for identification of LPV Box-Jenkins models, Automatica, Vol. 46, No. 6, (2010), pp. 959-967. (pdf) (errata) (link)
  61. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: Asymptotically optimal orthonormal basis functions for LPV system Identification, Automatica, Vol. 45, No. 6, (2009), pp. 1359-1370. (pdf) (link)
  62. Tóth, R. and D. Fodor: Speed sensorless mixed sensitivity linear parameter variant H_inf control of the induction motor, Journal of Electrical Engineering, Vol. 6, No. 4, (2006), pp. 12/1-6. (link)
  63. Tóth, R.: Simulation results on the asymptotic periodicity of compartmental systems with lags, Functional Differential Equations, Vol. 11, No. 1-2, (2004), pp. 195-202. (pdf) (link)

Submitted Journal Papers (6)

  1. Antal, P., T. Péni, and R. Tóth: Payload Grasping and Transportation by a Quadrotor with a Hook-Based Manipulator. Submitted to the IEEE Transactions on Control Systems Technology  (2024). (ELKH, ARNL) 
  2. Koelewijn, P.J.W.,  S. Weiland, and R. Tóth: Equilibrium-Independent Control of Continuous-Time Nonlinear Systems via the LPV Framework, Submitted to IEEE Transactions on Automatic Control (2023). (ERC, ARNL) (pdf is available upon request)
  3. Verhoek, C., R. Tóth and H. Abbas: Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems. Submitted to the IEEE Transactions on Automatic Control (2022). (ERC) (pdf is available upon request)
  4. Koelewijn, P.J.W., R. Tóth, H. Nijmeijer and S. Weiland: Nonlinear Tracking and Rejection using Linear Parameter-Varying Control, Submitted to International Journal of Robust and Nonlinear Control (2024). (ERC, ARNL) (pdf is available upon request)
  5. Liu, Y., P. Wang, and R. Tóth: Learning For Predictive Control: A Dual Gaussian Process Approach. Submitted to Automatica (2022). (ELKH) (pdf is available upon request).
  6. Verhoek, C., J. Berberich, S. Haesaert, R. Tóth and H. S. Abbas: A Linear Parameter-Varying Approach to Data Predictive Control, Submitted to IEEE Transactions on Automatic Control (2023). (ERC, ARNL) (pdf is available upon request)

Conference Proceedings (140)

  1. Szécsi, M., B. Györök, Á. Weinhardt-Kovács, G.I. Beintema, M. Schoukens, T. Péni, R. Tóth: Deep learning of vehicle dynamics, Invited paper, Accepted to the 20th IFAC Symposium on System Identification, (2024), Boston, USA. (JKK, FARADAI, AFOSR)
  2. Koelewijn, P.J.W., R. Singh, P. Seiler, R. Tóth: Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems, Accepted to the 20th IFAC Symposium on System Identification, (2024), Boston, USA. (Mathworks, ARNL)
  3. Liu, Y., R. Tóth, M. Schoukens: Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks, Invited paper, Accepted to the 20th IFAC Symposium on System Identification, (2024), Boston, USA.
  4. Kiss, M., R. Tóth, M. Schoukens: Space-Filling Input Design for Nonlinear State-Space Identification, Accepted to the 20th IFAC Symposium on System Identification, (2024), Boston, USA.
  5. Weigand, J., G. I. Beintema, J. Ulmen, D. Görges, R. Tóth, M. Schoukens, M. Ruskowski: State Derivative Normalization for Continuous-Time Deep Neural Networks, Accepted to the 20th IFAC Symposium on System Identification, (2024), Boston, USA.
  6. Champneys, M., G. I. Beintema, R. Tóth, M. Schoukens, T. Rogers: Baseline Results for Selected Nonlinear System Identification Benchmarks, Accepted to the 20th IFAC Symposium on System Identification, (2024), Boston, USA.
  7. Huijgevoort, B.C. van, C. Verhoek, R. Tóth, S. Haesaert: Direct data-driven control with signal temporal logic specifications, Accepted to the 8th IFAC Conference on Analysis and Design of Hybrid Systems, (2024) Boulder, Colorado, USA.
  8. Kon, J., J. van de Wijdeven, D. Bruijnen, R. Tóth, M. Heertjes, and T. Oomen: Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification, Accepted to the 22nd European Control Conference, (2024) Stockholm, Sweden. 
  9. Antal, P., T. Péni, and R. Tóth: Computationally Efficient Sampling-Based Algorithm for Stability Analysis of Nonlinear Systems, Accepted to the 22nd European Control Conference, (2024) Stockholm, Sweden. (ARNL)
  10. Floch, K., T. Péni, and R. Tóth: Gaussian Process Based Adaptive Trajectory Tracking Control for Autonomous Ground Vehicles, Accepted to the 22nd European Control Conference, (2024) Stockholm, Sweden. (FARADAI, AFOSR)
  11. Spin, L.M. , C. Verhoek, W.P.M.H. Heemels, N. van de Wouw, and R. Tóth: Unified Behavioral Data-Driven Performance Analysis A Generalized Plant Approach, Invited paper, Accepted to the 22nd European Control Conference, (2024) Stockholm, Sweden. (ARNL)
  12. Hoekstra, J. H.,  B. Cseppento, G. I. Beintema, M. Schoukens, Zs. Kollár, and R. Tóth: Computationally efficient predictive control based on ANN state-space models, Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 6330-6335. (ELKH, ARNL) 
  13. Broens, Y., H. Butler and R. Tóth: On improved commutation for moving-magnet planar actuators, Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 3688-3693. (IT2, ARNL) 
  14. Verhoek, C., P. J. W. Koelewijn, S. Haesaert, and R. Tóth: Direct data-driven state-feedback control of general nonlinear systems, Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 3688-3693. (ERC, ARNL) 
  15. Petreczky, M., R. Tóth, and G. Mercère: Minimal realizations of input-output behaviors by LPV state-space representations with affine dependency, Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 3694-3699. (ARNL)
  16. Shakib, M. F., R. Tóth, A. Y. Pogromsky, A. Pavlov, N. van de Wouw: Kernel-based learning of nonlinear state-space models with stability guarantees. Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 2897-2902. (ELKH) (pdf is available upon request)
  17. Kon, J., J. van de Wijdeven, D. Bruijnen, R. Tóth, M. Heertjes, and T. Oomen: Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach, Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 3720-3725. (pdf is available upon request)
  18. C. Verhoek, R. Wang, and R. Tóth: Learning Stable and Robust Linear Parameter-Varying State-Space Models, Proc. of the 62nd IEEE Conference on Decision and Control, (2023) Marina Bay Sands, Singapore, pp. 1348-1353. (ERC, ARNL) (pdf is available upon request)
  19. Iacob, L.C., M. Schoukens, and R. Tóth: Finite Dimensional Koopman Form of Polynomial Nonlinear Systems. Proc. of the IFAC World Congress, (2023) Yokohama, Japan, pp. 6423-6428. (ERC, AI4GNC) (pdf is available upon request)
  20. Ramkannan, R., G. I. Beintema, R. Tóth, and M. Schoukens: Initialization Approach for Nonlinear State-Space Identification via The Subspace Encoder Approach. Proc. of the IFAC World Congress, (2023) Yokohama, Japan, pp. 5146-5151. (ELKH) (pdf is available upon request)
  21. Moradi S., N. Jaensson, R. Tóth, and M. Schoukens: Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models. Proc. of the IFAC World Congress, (2023) Yokohama, Japan, pp. 5152-5157. (DAMOCLES) (pdf is available upon request)
  22. Verhoek, C., H. S. Abbas, and R. Tóth: Direct data-driven LPV control of nonlinear systems: An experimental result. Proc. of the IFAC World Congress, (2023) Yokohama, Japan, pp. 2263-2268. (ERC, AI4GNC) (pdf is available upon request)
  23. Vinjarapu, A.S.H., Y. Broens and R. Tóth: Exploring the use of deep learning in task-flexible ILC extension, Proc. of the American Control Conference, (2023), San Diego, CA, USA, pp. 2751-2756. (IT2, ARNL) (pdf is available upon request)
  24. Hewing, L., D. Gramlich, C. Verhoek, R. Polonio, J. Veenman, C. Ardura, R. Tóth, C. Ebenbauer, C. Scherer, and V. Preda: Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods, Proc. of the 12th International Conference on Guidance, Navigation & Control Systems, (2023), Sopot, Poland.
  25. Beintema, G.I., M. Schoukens, and R. Tóth: Continuous-Time Identification of Dynamic State-Space Models by Deep Subspace Encoding, Proc. of the International Conference on Learning Representations (ICLR), (2023), Kigali, Rwanda.
  26. Iacob, L. C., R. Tóth, M. Schoukens: Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs, Proc. of the 5th IFAC Workshop on Linear Parameter-Varying Systems, (2022), Montreal, Canada, pp. 250-255. (ERC, ARNL)
  27. de Lange, M. H., C. Verhoek, V. Preda, and R. Tóth: LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle, Proc. of the 5th IFAC Workshop on Linear Parameter-Varying Systems, (2022), Montreal, Canada, pp. 238-243. (AI4GNC, ARNL)
  28. Verhoek, C., G. I. Beintema, S. Haesaert, M. Schoukens, and R. Tóth:  Deep-Learning-Based Identification of LPV Models for Nonlinear Systems, Proc. of the 61st IEEE Conference on Decision and Control, Cancun, (2022), Cancun, Mexico, pp. 3274-3280. (AI4GNC, ERC)
  29. Broens, Y., H. Butler and R. Tóth: On modal observers for beyond rigid body H_inf control in high-precision mechatronics, Proc. of the 61st IEEE Conference on Decision and Control, Cancun, (2022), Cancun, Mexico, pp. 1722-1727. (IT2, ARNL)
  30. Antal, P., T. Péni and R. Tóth: Nonlinear Control Method for Backflipping with Miniature Quadcopters, Proc. of the IFAC Symposium on Intelligent Autonomous Vehicles, (2022), Prague, Czech Republic. (ELKH, ARNL)
  31. Bloemers, T.A.H., T.A.E. Oomen and R. Tóth: Frequency Response Data-driven LPV Controller Synthesis for MIMO Systems, Proc. of the American Control Conference, (2022), Atlanta, GA, USA, pp. 5205-5210. (ERC, ARNL)
  32. Broens, Y., H. Butler and R. Tóth: LPV sequential loop closing for high-precision motion systems, Proc. of the American Control Conference, (2022), Atlanta, GA, USA, pp. 3178-3183. (IT2, ARNL)
  33. Verhoek, C. R. Tóth, S. Haesaert and A. Koch: Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems, Proc. of the 60th IEEE conference on Decision and Control, (2021), Austin, Texas, USA, pp. 5033-5039. (ERC, AI4GNC)
  34. Liu, Y. and R. Tóth: Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process, Proc. of the 60th IEEE conference on Decision and Control, (2021), Austin, Texas, USA, pp. 1515-1521. (ARNL)
  35. Bosman Barros, C. P., H. Butler, R. Tóth, J. van de Wijdeven: On feedforward control of piezoelectric dual-stage actuator systems, Proc. of the 60th IEEE conference on Decision and Control, (2021), Austin, Texas, USA, pp. 5581-5587 .
  36. Iacob, L. C., G. I. Beintema, M. Schoukens and R. Tóth: Deep Identification of Nonlinear Systems in Koopman Form, Proc. of the 60th IEEE conference on Decision and Control, (2021), Austin, Texas, USA, pp. 2284-2289. [TC-SIAC/IC Outstanding Student Paper Prize], (MILAB)
  37. Koelewijn, P. J.W. and R. Tóth: Incremental Dissipativity based Control of Discrete-Time Nonlinear Systems using the Linear Parameter-Varying Framework, Proc. of  the 60th IEEE conference on Decision and Control, (2021), Austin, Texas, USA, pp. 3277-3282. (ERC)
  38. Verhoek, C., H. S. Abbas and R. Tóth: Data-Driven Predictive Control for Linear Parameter-Varying Systems. Proc. of the 4th IFAC Workshop on Linear Parameter-Varying Systems, (2021), Padova, Italy, pp. 101-108. (ERC, AI4GNC)
  39. Koelewijn, P. J.W., R. Tóth, S. Weiland: Incremental Stability and Performance Analysis of Discrete-Time Nonlinear Systems using the LPV Framework. Proc. of the 4th IFAC Workshop on Linear Parameter-Varying Systems, (2021), Padova, Italy, pp. 75-82. (ERC)
  40. Bloemers, T., R. Tóth, T. Oomen: Frequency-Domain Data-Driven Controller Synthesis for Unstable LPV Systems. Proc. of the 4th IFAC Workshop on Linear Parameter-Varying Systems, (2021), Padova, Italy, pp. 109-115. (ERC)
  41. G. Rödönyi, R. Tóth, D. Pup, A. Kisari, Zs. Vígh, P. Kőrös,J. Bokor: Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous car. Proc. of the 4th IFAC Workshop on Linear Parameter-Varying Systems, (2021), Padova, Italy, pp. 20-26.
  42. den Boef, P.,  P. B. Cox and R. Tóth: LPVcore: MATLAB Toolbox for LPV Modelling, Identification and Control Of Non-Linear Systems, Proc. of the 19th IFAC Symposium System Identification: learning models for decision and control, (2021),  Padova, Italy. (ERC)
  43. Nechita, S.-C., R. Tóth, D. Khandelwal and M. Schoukens: Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming, Proc. of the 19th IFAC Symposium System Identification: learning models for decision and control, (2021),  Padova, Italy.
  44. Nechita, S.-C., R. Tóth and K. van Berkel: Data-driven System Identification of Thermal Systems using Machine Learning, Proc. of the 19th IFAC Symposium System Identification: learning models for decision and control, (2021),  Padova, Italy.
  45. Beintema, G. I., R. Tóth and M. Schoukens: Non-linear State-space Model Identification from Video Data using Deep Encoders, Proc. of the 19th IFAC Symposium System Identification: learning models for decision and control, (2021),  Padova, Italy. (ERC)
  46. G. Rödönyi, G. I. Beintema, R. Tóth, M. Schoukens, D. Pup, A. Kisari, Zs. Vígh, P. Kőrös, A. Soumelidis, J. Bokor: Identification of the nonlinear steering dynamics of an autonomous vehicle, Proc. of the 19th IFAC Symposium System Identification: learning models for decision and control, (2021),  Padova, Italy.
  47. Beintema, G. I., R. Tóth and M. Schoukens: Nonlinear state-space identification using deep encoder networks. Proc. of Machine Learning Research (3rd Annual Learning for Dynamics & Control Conference), (2021) Zurich, Switzerland, Vol. 144, pp. 1-10.
  48. Proimadis, I., Y. Broens, R. Tóth and Hans Butler: Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system. Proc. of Machine Learning Research (3rd Annual Learning for Dynamics & Control Conference), (2021) Zurich, Switzerland, Vol. 144, pp. 1-12. (IT2)
  49. Bosman Barros, C. P. , H. Butler and R. Tóth: On the Use of the Smith-McMillan Form in Decoupling System Dynamics, Proc. of the American Control Conference, (2021), New Orleans, Louisiana, USA, pp. 2065-2070.
  50. Liu, Z., Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation, Proc. of the Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Autonomous Driving, Seattle, Washington, USA, 2020.
  51. Schoukens, M. and R. Tóth: On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation, Proc. of the IFAC World Congress, Berlin, 2020. (ERC)
  52. Shakib, M.F., R. Tóth, A.Y. Pogromsky, A. Pavlov and N. van de Wouw: State-Space Kernelized Closed-Loop Identification of Nonlinear Systems, Proc. of the IFAC World Congress, Berlin, (2020).
  53. Sadeghzadeh, A. and R. Tóth: Linear Parameter-Varying Embedding of Nonlinear Models with Reduced Conservativeness, Proc. of the IFAC World Congress, Berlin, (2020). (ERC)
  54. Koelewijn, P.J.W. and R. Tóth: Scheduling Dimension Reduction of LPV Models – A Deep Neural Network Approach, Proc. of the American Control Conference, (2020), Denver, USA, pp. 1111-1117. (ERC) (link)
  55. Koelewijn, P.J.W., G. S. Mazzoccante, R. Tóth and S. Weiland: Pitfalls of Guaranteeing Asymptotic Stability in LPV Control of Nonlinear Systems, Proc. of the European Control Conference, (2020), Saint Petersburg, Russia, pp. 1573-1578. (ERC) (link)
  56. Wang, R., R. Tóth and I. R. Manchester: A Comparison of LPV Gain Scheduling and Control Contraction Metrics for Nonlinear Control, Proc. of the 3rd IFAC Workshop on Linear Parameter-Varying Systems, (2019), Eindhoven, The Netherlands, pp. 38-43. (ERC)
  57. Schoukens, M. and R. Tóth: Frequency Response Functions of Linear Parameter-Varying Systems, Proc. of the 3rd IFAC Workshop on Linear Parameter-Varying Systems, (2019), Eindhoven, The Netherlands, pp. 32-37. (ERC)
  58. Bloemers, T., R. Tóth and T. Oomen: Data-Driven LPV Reference Tracking for a Control Moment Gyroscope, Proc. of the 3rd IFAC Workshop on Linear Parameter-Varying Systems, (2019), Eindhoven, The Netherlands, pp. 134-139. (ERC)
  59. Koelewijn, P.J.W., R. Tóth and H. Nijmeijer: Linear Parameter-Varying Control of Nonlinear Systems based on Incremental Stability, Proc. of the 3rd IFAC Workshop on Linear Parameter-Varying Systems, (2019), Eindhoven, The Netherlands, pp. 38-43. (ERC)
  60. Gángó, D., T. Péni  and R. Tóth: Learning based Approximate Model Predictive Control for constrained qLPV systems, Proc. of the 3rd IFAC Workshop on Linear Parameter-Varying Systems, (2019), Eindhoven, The Netherlands, pp. 152-157. (ERC)
  61. Boef, den P., R. Tóth and M. Schoukens: On Behavioral Interpolation in Local LPV System Identification, Proc. of the 3rd IFAC Workshop on Linear Parameter-Varying Systems, (2019), Eindhoven, The Netherlands, pp. 20-25. (ERC)
  62. Bloemers, T.A.H., R. Tóth and T. Oomen: Towards Data-Driven LPV Controller Synthesis based on Frequency Response Function, Proc. of the 58th IEEE Conference on Decision and Control, (2019), Nice, France, pp 5680-5685. (ERC)
  63. Custers, C.H.H.M., I. Proimadis, J.W. Jansen, H. Butler, R. Tóth, E.A. Lomonova and P.M.J. van den Hof: Active Compensation of the Deformation of a Magnetically Levitated Mover of a Planar Motor, Proc. of the IEEE International Electric Machines and Drives Conference, (2019), San Diego, USA, pp. 854-861.
  64. Khandelwal, D., M. Schoukens and R. Tóth: Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming, Proc. of the IEEE Congress on Evolutionary Computation, (2019), Wellington, New Zealand, pp. 2673-2680.
  65. Khandelwal, D., M. Schoukens and R. Tóth: Grammar-based Representation and Identification of Dynamical Systems, Proc. of the European Control Conference, (2019), Naples, Italy, pp. 1318-1323.
  66. Mejari, M., D. Piga, R. Tóth and A. Bemporad: Kernelized Identification of Linear Parameter-Varying Models with Linear Fractional Representation,  Proc. of the European Control Conference, (2019), Naples, Italy, pp. 337-342.
  67. Khandelwal, D., M. Schoukens and R. Tóth: On the Simulation of Polynomial NARMAX Models, Proc. of the 57th IEEE Conference on Decision and Control, (2018), Miami Beach, FL, USA, pp. 1445-1550.  (arxiv)
  68. Wiel, T. T. R. van de, R. Tóth and V. I. Kiriouchine: Comparison of Parameter-Varying Decoupling Based Control Schemes for a Quadcopter, Proc. of the IFAC Workshop on Linear Parameter Varying Systems, (2018), Florianopolis, Brazil, pp. 156-162. (ERC) (pdf) (link)
  69. Koelewijn, P. J. W., P. S. G. Cisneros, H. Werner and R. Tóth: LPV Control of a Gyroscope with Inverted Pendulum Attachment, Proc. of the IFAC Workshop on Linear Parameter Varying Systems, (2018), Florianopolis, Brazil, pp. 150-155. (ERC) (pdf) (link)
  70. Abbas, H. S., J. Hanema, R. Tóth, J. Mohammadpour and N. Meskin: A New Approach to Robust MPC Design for LPV Systems in Input-Output Form, Proc. of the IFAC Workshop on Linear Parameter Varying Systems, (2018), Florianopolis, Brazil, pp. 392-397. (pdf) (link)
  71. Schoukens, M. and R. Tóth: Linear Parameter Varying Representation of a class of MIMO Nonlinear Systems, Proc. of the IFAC Workshop on Linear Parameter Varying Systems, (2018), Florianopolis, Brazil, pp. 271-276. (ERC) (pdf) (script) (link) (arxiv)
  72. Bloemers, T.A.H., I.Proimadis, Y. Kasemsinsup and R. Tóth: Parameter-Dependent Feedforward Strategies for Motion Systems, Proc. of the American Control Conference, (2018) Milwaukee, WI, USA, pp. 2017-2022. (pdf) (link)
  73. Schoukens, M. and R. Tóth: From Nonlinear Identification to Linear Parameter Varying Models: Benchmark Examples, Proc. of the IFAC Symposium on System Identification, (2018), Stockholm, Sweden, pp. 419-424. (ERC) (pdf) (link) (arxiv)
  74. Hanema, J., R. Tóth, M. Lazar: Stabilizing Non-linear MPC Using Linear Parameter-Varying Representations, Proc. of the 56th IEEE Conference on Decision and Control, (2017), Melbourne, Australia, pp. 3582-3587. (pdf) (link)
  75. Schulz, E., P. B. Cox, H. Werner, R. Tóth: LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods, Proc. of the 56th IEEE Conference on Decision and Control, (2017), Melbourne, Australia, pp. 3575-3581. (pdf) (link)
  76. Darwish, M.A.H., J. Lataire and R. Tóth: Bayesian Frequency Domain Identification of LTI Systems with OBFs Kernels, Proc. of the 20th IFAC World Congress, (2017), Toulouse, France, pp. 6412-6417. (pdf) (link)
  77. Hanema, J., M. Lazar and R. Tóth: Tube-based LPV constant output reference tracking MPC with error bound, Proc. of the 20th IFAC World Congress, (2017), Toulouse, France, pp. 8942-8947. (pdf) (link)
  78. Chitraganti, S., R. Tóth, N. Meskin and J. Mohammadpour: Stochastic model predictive control for LPV systems, Proc. of the American Control Conference, (2017) Seattle, WA, USA, pp. 5654-5659. (pdf) (link)
  79. Hanema, J., R. Tóth, M. Lazar: Tube-based anticipative model predictive control for linear parameter-varying systems, Proc. of the 55th IEEE Conference on Decision and Control, (2016) Las Vegas, USA, pp. 1458-1463. (pdf) (link)
  80. Cox, P. B. and R. Tóth: Alternative Form of Predictor Based Identification of LPV-SS Models with Innovation Noise, Proc. of the 55th IEEE Conference on Decision and Control, (2016) Las Vegas, USA, pp. 1223-1228. (pdf) (link)
  81. Hanema, J., R. Tóth, M. Lazar and H. S. Abbas: MPC for Linear Parameter-Varying Systems in Input-Output Representation, IEEE International Symposium on Intelligent Control, (2016) Buenos Aires, Argentina, pp. 354-359. (pdf) (link)
  82. Golabi, A., M. Davoodi, N. Meskin, R. Tóth and J. Mohammadpour: Event-triggered Fault Detection for Discrete-time LPV Systems, Proc. of the conference on Event-Based Control, Communication and Signal Processing, (2016) Krakow, Poland. (pdf) (link)
  83. Cox, P., R. Tóth and M. Petreczky: LPV State-Space Model Identification in a Bayesian Setting: a 3-step Procedure, Proc. of the American Control Conference, (2016) Boston, MA, USA, pp. 4604-4610. (pdf) (link) (Matlab example)
  84. Golabi, A., N. Meskin, R. Tóth, J. Mohammadpour and T. Donkers: Event-triggered Control for Discrete-time LPV Systems, Proc. of the American Control Conference, (2016) Boston, MA, USA, pp. 3680-3685. (pdf) (link)
  85. Liu, Q., J. Mohammadpour, R. Tóth, and N. Meskin: Non-Parametric Identification of Parameter-Varying Spatially-Interconnected Systems Using an LS-SVM Approach, Proc. of the American Control Conference, (2016) Boston, MA, USA, pp. 4592-4597. (pdf) (link)
  86. Baştuğ, M., M. Petreczky, R. Tóth, R. Wisniewski, J. Leth and Denis Efimov: Moment Matching Based Model Reduction for LPV State-Space Models, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 5334-5338. (pdf) (link)
  87. Bachnas, A.A., S. Weiland and R. Tóth: Data Driven Predictive Control Based on OBF Model Structures, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 3026-3031. (pdf) (link)
  88. Abbas, H.S., R. Tóth, Nader Meskin, Javad Mohammadpour and Jurre Hanema: An MPC Approach for LPV Systems in Input-Output Form, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 91-96. (pdf) (link)
  89. Darwish, M., P. Cox, G. Pillonetto and R. Tóth: Bayesian Identification of LPV Box-Jenkins Models, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 66-71. (pdf) (link)
  90. Darwish, M., G. Pillonetto and R. Tóth: Perspectives of Orthonormal Basis Functions Based Kernels in Bayesian System Identification, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 2713-2718. (pdf) (link)
  91. Abbasi, F., J. Mohammadpour, R. Tóth and Nader Meskin: A Bayesian Approach for Model Identification of LPV Systems with Uncertain Scheduling Variables, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 789-794. (pdf) (link)
  92. Rizvi, S. Z., J. Mohammadpour, R. Tóth and Nader Meskin: An IV-SVM-based Approach for Identification of State-space LPV Models under Generic Noise Conditions, Proc. of the 54th IEEE Conference on Decision and Control, (2015) Osaka, Japan, pp. 7380-7385. (pdf) (link)
  93. Rahme, S., H. S. Abbas, N. Meskin, R. Tóth and J. Mohammadpour: Reduced LPV Model Development and Control of a Solution Copolymerization Reactor, Proc. of the IEEE Multi-Conference on Systems and Control, (2015) Sydney, Australia, pp. 1044-1050. (pdf) (link)
  94. Rahme, S., H.S. Abbas, N. Meskin, C. Hoffmann, R. Tóth and J. Mohammadpour: Linear Parameter-Varying Control of a Copolymerization Reactor, Proc. of the 1st IFAC Workshop on Linear Parameter-Varying Systems, (2015) Grenoble, France, pp. 200-206. (pdf) (link)
  95. Rizvi, S.Z., J. Mohammadpour, R. Tóth and N. Meskin: A Kernel-based Approach to MIMO LPV State-space Identification and Application to a Nonlinear Process System Proc. of the 1st IFAC Workshop on Linear Parameter-Varying Systems, (2015) Grenoble, France, pp. 85-90. (pdf) (link)
  96. Cox, P.B., R. Tóth and M. Petreczky: Estimation of LPV-SS Models with Static Dependency using Correlation Analysis, Proc. of the 1st IFAC Workshop on Linear Parameter-Varying Systems, (2015) Grenoble, France, pp. 91-96. (pdf) (link)
  97. Formentin, S., D. Piga, R. Tóth and S. Savaresi: Nonparametric LPV data-driven control, Proc. of the 1st IFAC Workshop on Linear Parameter-Varying Systems, (2015) Grenoble, France, pp. 146-151. (pdf) (link)
  98. Larimore, W.E., P.B. Cox, R. Tóth: CVA Identification of Nonlinear Systems with LPV State-Space Models of Affine Dependence, Proc. of the American Control Conference, (2015) Chicago, IL, USA, pp. 831-837. (pdf) (link)
  99. R. Duijkers, R. Tóth, D. Piga and V. Laurain: Shrinking Complexity of Scheduling Dependencies in LS-SVM Based LPV System Identification, Invited paper, Proc. of the 53rd IEEE Conference on Decision and Control, (2014) Los Angeles, CA, USA, pp. 2561-2566. (pdf) (link)
  100. A. Golabi, N. Meskin, R. Tóth and M. Mohammadpour: A Bayesian Approach for Estimation of LPV Linear-Regression Models, Invited paper, Proc of the 53rd IEEE Conference on Decision and Control, (2014) Los Angeles, CA, USA, pp. 2555-2560. (pdf) (link)
  101. Abbas, H.S., R. Tóth, M. Petreczky, N. Meskin and J. Mohammadpour: Embedding of Nonlinear Systems in a Linear Parameter-Varying Representation, Proc. of the 19th IFAC World Congress, (2014) Cape Town, South Africa, pp. 6907-6913. (pdf) (link)
  102. Lataire, J., D. Piga and R. Tóth: Frequency-domain least-squares support vector machines to deal with correlated errors when identifying linear time-varying systems, Proc. of the 19th IFAC World Congress, (2014) Cape Town, South Africa, pp. 10024-10029. (pdf) (link)
  103. Rizvi, S. Z., J. Mohammadpour, R. Tóth and N. Meskin: Parameter Set-mapping using Kernel-based PCA for Linear Parameter Varying Systems, Proc. of the 13th European Control Conference, (2014) Strasbourg, France, pp. 2744-2749. (pdf) (link)
  104. Abbasi, F., J. Mohammadpour, R. Tóth and N. Meskin: A support vector machine-based method for LPV-ARX identification with noisy scheduling parameters. Proc. of the 13th European Control Conference, (2014) Strasbourg, France, pp. 370-375. (pdf) (link)
  105. Wollnack, S., H. S. Abbas, H. Werner and R. Tóth: Fixed-Structure LPV Controller Synthesis based on Implicit Input Output Representations, Proc. of the 52nd IEEE Conference on Decision and Control, (2013) Florence, Italy, pp. 2103-2108. (pdf) (link)
  106. Piga, D. and R. Tóth: LPV model order selection in an LS-SVM setting, Invited paper, Proc. of the 52nd IEEE Conference on Decision and Control, (2013) Florence, Italy, pp. 4128-4133. (pdf) (link)
  107. Formentin, S., D. Piga, R. Tóth and S. M. Savaresi: Direct data-driven control of linear parameter-varying systems, Invited paper, Proc. of the 52nd IEEE Conference on Decision and Control, (2013) Florence, Italy, pp. 4110-4115. (pdf) (link)
  108. Bachnas, A. A., R. Tóth, A. Mesbah and J. Ludlage:  Perspectives of data-driven LPV modeling of high-purity distillation columns, Invited paper, Proc. of the European Control Conference, (2013) Zurich, Switzerland, pp. 3776-3783. (pdf) (link)
  109. Tóth, R., H. Hjalmarsson and C. R. Rojas: Order and Structural Dependence Selection of LPV-ARX Models Revisited, Invited paper, Proc. of the 51st IEEE Conference on Decision and Control, (2012) Maui, Hawaii, USA, pp. 6271-6276. (pdf) (link)
  110. Siraj, M. M., R. Tóth, S. Weiland: Joint order and dependency reduction for LPV state-space models, Invited paper, Proc. of the 51st IEEE Conference on Decision and Control, (2012) Maui, Hawaii, USA, pp. 6291-6296. (pdf) (link)
  111. Cerone, V., D. Piga, D. Regruto and R. Tóth: Fixed order LPV controllers design for LPV models in input-output form, Invited paper, Proc. of the 51st IEEE Conference on Decision and Control, (2012) Maui, Hawaii, USA, pp. 6297-6302. (pdf) (link)
  112. Tóth, R., H. Hjalmarsson and C. R. Rojas: Sparse estimation of polynomial dynamical models, Invited paper, Proc. of the 16th IFAC Symposium on System Identification, (2012) Brussels, Belgium, pp. 983-988 . (pdf) (link)
  113. Laurain, V., R. Tóth, W-X. Zheng and M. Gilson: Nonparametric identification of LPV models under general noise conditions: an LS-SVM based approach, Invited paper, Proc. of the 16th IFAC Symposium on System Identification, (2012) Brussels, Belgium, pp. 1761-1766. (pdf) (link)
  114. Cerone, V., D. Piga, D. Regruto and R. Tóth: Input-output LPV model identification with guaranteed quadratic stability, Invited paper, Proc. of the 16th IFAC Symposium on System Identification, (2012) Brussels, Belgium, pp. 1767-1772. (pdf) (link)
  115. Cerone, V., D. Piga, D. Regruto and R. Tóth: Minimal LPV state-space realization driven set-membership identification. Proc. of the American Control Conf., (2012) Montréal, Canada, pp. 3421-3426. (pdf) (link)
  116. Dankers, A. G., R. Tóth, P. S. C. Heuberger, X. Bombois and P. M. J. Van den Hof: Informative data and identifiability in LPV-ARX prediction error identification, Proc. of the 50th IEEE Conference on Decision and Control, (2011) Orlando, Florida, USA, pp. 799-804. (pdf) (link)
  117. Laurain, V., W-X. Zheng and R. Tóth: Introducing instrumental variables in the LS-SVM based identification framework, Proc. of the 50th IEEE Conference on Decision and Control, (2011) Orlando, Florida, USA, pp. 3198-3203. (pdf) (link)
  118. Tóth, R., V. Laurain, W-X. Zheng and K. Poolla: Model structure learning: A support vector machine approach for LPV linear-regression models, Proc. of the 50th IEEE Conference on Decision and Control, (2011) Orlando, Florida, USA, pp. 3192-3197. (pdf) (errata) (link)
  119. Tóth, R., B. M. Sanandaji, K. Poolla and T. L. Vincent: Compressive system identification in the linear time-invariant framework, Proc. of the 50th IEEE Conference on Decision and Control, (2011) Orlando, Florida, USA, pp. 783-790. (pdf) (link)
  120. Sanandaji, B. M., T. L. Vincent, M. B. Wakin, R. Tóth and K. Poolla: Compressive system identification of LTI and LTV ARX models: The limited data set case, Proc. of the 50th IEEE Conference on Decision and Control, (2011) Orlando, Florida, USA, 791-798. (pdf) (link)
  121. Tóth, R., V. Laurain, M. Gilson and H. Garnier: On the closed loop identification of LPV models using instrumental variables. Proc. of the 18th IFAC World Congress, (2011), Milano, Italy, pp. 7773-7778 (pdf) (link)
  122. Kulcsár, B. and R. Tóth: On the similarity state transformation for linear parameter-varying systems. Proc. of the 18th IFAC World Congress, (2011), Milano, Italy, pp. 4155-4160. (pdf) (link)
  123. Tóth, R., M. van de Wal, P. S. C. Heuberger and P. M. J. Van den Hof: LPV Identification of High Performance Positioning Devices. Invited paper, Proc. of the American Control Conf., (2011) San Francisco, California, USA, pp. 151-158. (pdf) (link)
  124. Laurain, V., M. Gilson, R. Tóth and H. Garnier: Direct identification of continuous-time LPV models. Invited paper, Proc. of the American Control Conf., (2011) San Francisco, California, USA, pp. 159-164. (pdf) (link)
  125. Tóth, R., P. M. J. Van den Hof, J. H. A. Ludlage and P. S. C. Heuberger: Identification of nonlinear process models in an LPV framework. Proc. of the 9th International Symp. on Dynamics and Control of Process Systems, (2010) Leuven, Belgium, pp. 869-874. (pdf) (link)
  126. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: A prediction-error identification framework for linear parameter-varying systems. Invited paper, Proc. of the 19th International Symposium on Mathematical Theory of Networks and Systems, (2010) Budapest, Hungary, pp. 1351-1352. (pdf)
  127. Abbas H., R. Tóth, and H. Werner, State-space realization of LPV input-output models: practical methods for the user, Invited paper, Proc. of the American Control Conf., (2010) Baltimore, Maryland, USA, pp. 3883-3888. (pdf) (link)
  128. Laurain, V., M. Gilson, R. Tóth, and H. Garnier, Identification of LPV Output-Error and Box-Jenkins Models via Optimal Refined Instrumental Variable Methods, Invited paper, Proc. of the American Control Conf., (2010) Baltimore, Maryland, USA, pp. 3865-3870. (pdf) (link)
  129. Tóth, R., M. Lovera, P. S. C. Heuberger and P. M. J. Van den Hof: Discretization of Linear Fractional Representations of LPV systems, Proc. of the 48th IEEE Conference on Decision and Control, (2009) Shanghai, China, pp. 7424-7429. (pdf) (link)
  130. Tóth, R., C. Lyzell, M. Enqvist, P. S. C. Heuberger and P. M. J. Van den Hof: Order and Structural Dependence Selection of LPV-ARX Models Using a Nonnegative Garrote Approach, Proc. of the 48th IEEE Conference on Decision and Control, (2009) Shanghai, China, pp. 7406-7411. (pdf) (link)
  131. Tóth, R., Jan C. Willems, P. S. C. Heuberger and P. M. J. Van den Hof: A behavioral approach to LPV systems, Proc. of the European Control Conference, (2009) Budapest, Hungary, pp. 2015-2020. (pdf) (link)
  132. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: An LPV identification Framework Based on Orthonormal Basis Functions. Proc. of the 15th IFAC Symposium on System Identification, (2009) St. Malo, France, pp. 1328-1333. (pdf) (link)
  133. Khalate, A. A., X. Bombois, R. Tóth and R. Babuška: Optimal experimental design for LPV identification using a local approach. Proc. of the 15th IFAC Symposium on System Identification, (2009) St. Malo, France, pp. 162-167. (pdf) (link)
  134. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: Flexible model structures for LPV identification with static scheduling dependency, Invited paper, Proc. of the 47th IEEE Conference on Decision and Control, (2008) Cancun, Mexico, pp. 4522-4527. (pdf) (link)
  135. Tóth, R., F. Felici, P. S. C. Heuberger and P. M. J. Van den Hof: Crucial aspects of zero-order hold LPV state-space system discretization, Proc. of the 17th IFAC World Congress, (2008) Seoul, Korea, pp. 4952-4957. (pdf) (link)
  136. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: LPV system identification with globally fixed orthonormal basis functions, Proc. of the 46th IEEE Conference on Decision and Control, (2007) New Orleans, USA, pp. 3646-3653. (pdf) (link)
  137. Tóth, R., F. Felici, P. S. C. Heuberger and P. M. J. Van den Hof: Discrete time LPV I/O and state-space representations, differences of behavior and pitfalls of interpolation, Proc. of the European Control Conference, (2007) Kos, Greece, pp. 5418-5425.  (pdf) (errata) (link)
  138. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: Orthonormal basis selection for LPV system identification, the Fuzzy-Kolmogorov c-Max approach, Proc. of the 45th IEEE Conf. on Decision and Control, (2006) San Diego, USA, pp. 2529-2534. (pdf) (link)
  139. Tóth, R., P. S. C. Heuberger, and P. M. J. Van den Hof: Optimal pole selection for LPV system identification with OBFs, a clustering approach, Proc. of the 14th IFAC Symposium on System Identification, (2006) Newcastle, Australia, pp. 356-361. (pdf) (link)
  140. Tóth, R. and D. Fodor: Speed Sensorless mixed sensitivity linear parameter variant H_inf control of the induction motor, Proc. of the 43rd IEEE Conference on Decision and Control, (2004) Nassau, The Bahamas, pp. 4435-4440. (pdf) (link)

Submitted Conference Papers (7)

  1. Markovsky, I., C. Verhoek, and R. Tóth: The most powerful unfalsified linear parameter-varying model, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.
  2. Broens, Y., H. Butler and R. Tóth: Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control, Invited paper, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.
  3. Kon, J., R. Tóth, J. van de Wijdeven, M. Heertjes, T. Oomen: Structured Linear Input-Output Models with Stability Guarantees, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.
  4. Verhoek, C., J. Eising, F. Dörfler, and R. Tóth: Merging informativity and parameter-varying Lyapunov functions in data-driven LPV control, Invited paper, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.
  5. Hoekstra, J.H., C. Verhoek, R. Tóth, M. Schoukens: LFR-based data-driven model augmentation of physical models, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.
  6. Olucha, E.J., B. Terzin, A. Das, R. Tóth: On the reduction of Linear Parameter-Varying State-Space models, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.
  7. Baltussen, T.M.J.T., A. Katriniok, E. Lefeber, R. Tóth, W.P.M.H. Heemels: Online Learning Interaction Dynamics with Gaussian Processes-Based Dual Model Predictive Control for Multi-Agent Systems, Submitted to the 63rd IEEE Conference on Decision and Control , (2024), Milan, Italy.

Plenary Talks (2)

R. Tóth: Nonlinear Tracking & Rejection Using LPV Control: Towards LPV 2.0. Plenary talk at the 4th IFAC Workshop on Linear Parameter-Varying Systems, (2021) Milan, Italy.

R. Tóth: Finding the golden mean in data-driven modeling. Plenary talk at the 16th IFAC Symposium on System Identification, (2012) Brussels. (pptx)

Abstracts (63)

  1. Moradi, S., N. Jaensson, R. Tóth and M. Schoukens: Learning physical models using Hamiltonian Neural Networks with output error noise models, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 37.
  2. Beintema, G. I., R. Tóth and M. Schoukens: Meta-state-space: A new perspective on
    representing and identifying stochastic systems, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 98.
  3. Spin, L. M., R. Tóth, N. Van de Wouw and W. P. M. H. Heemels: Performance Shaping for Data-Driven Generalized plants, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 130.
  4. Iacob, L. C., M. Schoukens and R. Tóth: Embedding of Polynomial Nonlinear Systems into Finite Dimensional Koopman Representations, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 149.
  5. Verhoek, C., S. Haesaert, and R. Tóth: An experimental result on direct data-driven control of nonlinear systems using the LPV framework, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 214.
  6. Broens, Y., H. Butler and R. Tóth: Data-based electromagnetic calibration approaches for moving-magnet planar actuator systems, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 222.
  7. Retzler, A., R. Tóth, J. Swevers, J.-P. Noe, Zs. Kollár, G. I. Beintema, J. Weigand and M. Schoukens: Augmented model identification for forward simulation of a robot arm, Proc. of the 42nd Benelux Meeting, (2023) Elspeet, The Netherlands, pp. 213.
  8. Iacob, L. C., G. I. Beintema, M. Schoukens and R. Tóth: Deep Learning-based Identification of Koopman Models with Inputs, Proc. of the 41st Benelux Meeting, (2022) Brussels, Belgium, pp. 49.
  9. Beintema, G. I., R. Tóth and M. Schoukens: Continuous-time system identification by deep subspace encoders, Proc. of the 41st Benelux Meeting, (2022) Brussels, Belgium, pp. 51.
  10. Verhoek, C., G. Beintema, S. Haesaert, M. Schoukens and R. Tóth: Learning-Based Model-Augmentation of Nonlinear Approximative Models using the Sub-Space Encoder, Proc. of the 41st Benelux Meeting, (2022) Brussels, Belgium, pp. 52.
  11. Moradi, S., N. Jaensson, R. Tóth and M. Schoukens: Learning Constitutive Laws in Engineering Systems, Proc. of the 41st Benelux Meeting, (2022) Brussels, Belgium, pp. 83.
  12. Broens, Y., H. Butler and R. Tóth: On discretization of continuous-time LPV control solutions, Proc. of the 41st Benelux Meeting, (2022) Brussels, Belgium, pp. 199.
  13. Retzler, A., G. I. Beintema, M. Schoukens, R. Tóth, J. Swevers, and Zs. Kollár: Learning-based augmentation of mechatronic system models by deep subspace encoders, Proc. of the 41st Benelux Meeting, (2022) Brussels, Belgium, pp. 53.
  14. Iacob, L. C., R. Tóth and M. Schoukens: Learning Linear Surrogate Models of Nonlinear Systems, Proc. of the 40th Benelux Meeting, (2021) Rotterdam, The Netherlands.
  15. Verhoek, C., R. Tóth and S. Haesaert: Data-Driven Predictive Control of Linear Parameter-Varying Systems, Proc. of the 40th Benelux Meeting, (2021) Rotterdam, The Netherlands.
  16. Beintema, G. I., R. Tóth and M. Schoukens: Dynamical system identification from video using sub-space encoders, Proc. of the 40th Benelux Meeting, (2021) Rotterdam, The Netherlands.
  17. Broens, Y., H. Butler and R. Tóth: Improved commutation methods for moving-magnet planar actuators, Proc. of the 40th Benelux Meeting, (2021) Rotterdam, The Netherlands.
  18. Y. Liu and R. Tóth: Gaussian Processes Based Learning Control for Quadcopters. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 68.
  19. Iacob, L.C., R. Tóth and M. Schoukens: LPV Modeling Using the Koopman Operator. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 133.
  20. Shakib, F., R. Tóth, S. Pogromsky, A. Pavlov and N. van de Wouw: Non-Parametric Kernelized Identification of Closed Loop Nonlinear Systems. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 55.
  21. Sadeghzadeh, A. and R. Tóth: Linear Parameter-Varying Embedding of Nonlinear Models Based on Polynomial Approximation. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 79.
  22. Koelewijn, P. J. W., R. Tóth and S. Weiland: Incremental Stability based Analysis and Control of Nonlinear Systems using the LPV Framework. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 77.
  23. G. Beintema, R. Tóth and M. Schouken: Comparison of deep learning methods for system identification. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 157.
  24. Bloemers, T.A.H., R. Tóth and T. Oomen: Data-Driven Rational LPV Controller Synthesis for Unstable Systems using Frequency Response Functions. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 144.
  25. Bosman Barros, C. P., H. Butler , R. Tóth and K. van Berkel: Motion control of Piezo-electric actuators for nanopositioning. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 125.
  26. Verhoek, C., P. J. W. Koelewijn and R. Tóth: Incremental Dissipativity Analysis of Nonlinear Systems using the Linear Parameter-Varying Framework. Proc. of the 39th Benelux Meeting, (2020) Elspeet, The Netherlands, pp. 75.
  27. Bloemers, T.A.H., R. Tóth and T. Oomen:
    Data-driven LPV controller synthesis in the frequency-domain. Proc. of the European Research Network on System Identification, (2019), Maastricht, The Netherlands.
  28. Barros, C. P. B., R. Tóth and H. Butler: Modeling and Identification of Piezoelectric Actuators: Vibration, Creep and Hysteresis. Proc. of the European Research Network on System Identification, (2019), Maastricht, The Netherlands.
  29. Khandelwal, D., M. Schoukens and R. Tóth: Automating System Identification Using Grammar and Genetic Programming, Proc. of The Workshop on Nonlinear System Identification Benchmarks, (2019) Eindhoven, pp. 41.
  30. Schoukens, M. and R. Tóth: Identification of Nonlinear LFR Systems starting from the Best Linear Approximation, Proc. of The Workshop on Nonlinear System Identification Benchmarks, (2019) Eindhoven, pp. 44.
  31. Bloemers, T., R. Tóth and T. Oomen: Data-driven LPV synthesis: FIR controller case, Proc. of the 38th Benelux Meeting, (2019) Lommel, pp. 111.
  32. Koelewijn, P. and R. Tóth: Modeling for LPV Control, Proc. of the 38th Benelux Meeting, (2019) Lommel, pp. 152.
  33. Mazzoccante, G.S., R. Tóth and S. Weiland: On guaranteeing asymptotic output tracking and disturbance rejection for nonlinear systems with LPV control, Proc. of the 38th Benelux Meeting, (2019) Lommel, pp. 88.
  34. Khandelwal, D., M. Schoukens and R. Tóth: Automating System Identification: A Linguistic Approach, Proc. of the 38th Benelux Meeting, (2019) Lommel, pp. 21.
  35. Bosman Barros, C.P., H. Butler, R. Tóth and K. van Berkel: Modeling and control of a piezo-electric short-range stage, Proc. of the 38th Benelux Meeting, (2019) Lommel, pp. 174.
  36. Cox, P. B., R. Tóth and P. M. J. Van den Hof: Subspace Identification for Linear Parameter-Varying Systems, Proc. of the 37th Benelux Meeting, (2018) Soesterberg, The Netherlands, pp. 19.
  37. Schoukens, M. and R. Tóth: From Nonlinear Identification to Linear Parameter Varying Models: Benchmark Examples, Proc. of the 37th Benelux Meeting, (2018) Soesterberg, The Netherlands, pp. 68.
  38. Hanema, J., R. Tóth, M. Lazar: Tube-based linear parameter-varying MPC for a thermal system, Proc. of the 37th Benelux Meeting, (2018) Soesterberg, The Netherlands, pp. 106.
  39. Mazzoccante, G. S., R. Tóth and S. Weiland: On guaranteeing tracking performance and stability with LPV control for nonlinear systems, Proc. of the 37th Benelux Meeting, (2018) Soesterberg, The Netherlands, pp. 146.
  40. Khandelwal, D., R. Tóth, P.M.J. Van den Hof: Grammar-based Encoding of Well-posed Model Structures for Data-Driven Modeling, Proc. of the 37th Benelux Meeting, (2018) Soesterberg, The Netherlands, pp. 69.
  41. Hanema, J., R. Tóth, M. Lazar: Tube-based anticipative linear parameter-varying MPC: application to non-linear systems, Proc. of the 36th Benelux Meeting, (2017) Spa, Belgium, pp. 115.
  42. Proimadis, I., and R. Tóth: Modelling of a nanometer-accurate planar actuation system, Proc. of the 36th Benelux Meeting, (2017) Spa, Belgium, pp. 92.
  43. Darwish, M.A.H.,  J. Lataire, R. Tóth and P. M. J. Van den Hof: Bayesian Frequency Domain Identification of LTI Systems with OBFs Kernels, Proc. of the 36th Benelux Meeting, (2017) Spa, Belgium, pp. 18.
  44. Proimadis, I., and R. Tóth: Nanometer-accurate planar actuation system (NAPAS), Proc. of the 36th Benelux Meeting, (2016) Soesterberg, The Netherlands, pp. 156.
  45. Bachnas, A.A., S. Weiland and R. Tóth: Characterization of the tracking error for OBF based MPC, Proc. of the 36th Benelux Meeting, (2016) Soesterberg, The Netherlands, pp. 119.
  46. Khandelwal, D., and R. Tóth: Data-driven modelling using symbolic regression, Proc. of the 36th Benelux Meeting, (2016) Soesterberg, The Netherlands, pp. 111.
  47. Darwish, M.A.H., S. Chitraganti, T.B. Schön, R. Tóth and P.M.J. van den Hof: Maximum likelihood estimation of LPV-SS models: A Sequential Monte-Carlo approach, Proc. of the 36th Benelux Meeting, (2016) Soesterberg, The Netherlands, pp. 81.
  48. Cox, P.B., R. Tóth and P.M.J. van den Hof:  On the connection between different noise structures for LPV-SS models, Proc. of the 35th Benelux Meeting, (2016) Soesterberg, The Netherlands, pp. 78.
  49. Hanema, J., R. Tóth, M. Lazar and S. Weiland: Towards anticipative LPV tube model predictive control, Proc. of the 35th Benelux Meeting, (2016) Soesterberg, The Netherlands, pp. 67.
  50. Cox, P., R. Tóth and P.M.J. Van den Hof:  Estimation of LPV-SS Models with Static Dependency, Proc. of the 34th Benelux Meeting, (2015) Lommel, Belgium.
  51. Darwish M.A.H., R. Tóth and P.M.J. Van den Hof:  Selecting Shaping Kernels in Bayesian Identification of LTI Systems: An Orthonormal Basis Functions Approach, Proc. of the 34th Benelux Meeting, (2015) Lommel, Belgium.
  52. Hanema J., R. Tóth M. Lazar and S. Weiland:  Anticipative linear parameter-varying model predictive control, Proc. of the 34th Benelux Meeting, (2015) Lommel, Belgium.
  53. Bachnas, A.A., S.Weiland and R. Tóth: Data driven MPC based on OBF model structures, Proc. of the 34th Benelux Meeting, (2015) Lommel, Belgium.
  54. Cox, P., R. Tóth and P.M.J. Van den Hof: Identification of Linear Parameter Varying Input-Output Models with Noisy Measurements of the Scheduling Variable, Proc. of the 33rd Benelux Meeting, (2014) Heijden, The Netherlands, pp. 75.
  55. Darwish, M. A. H., R. Tóth and P.M.J. Van den Hof: Learning Models and Controllers from Data, Proc. of the 33rd Benelux Meeting, (2014) Heijden, The Netherlands, pp. 68.
  56. Bachnas, A. A., S. Weiland and R. Tóth: Uncertainty reduction techniques via orthonormal basis function based control synthesis, Proc. of the 33rd Benelux Meeting, (2014) Heijden, The Netherlands, pp. 90.
  57. Tóth, R., V. Laurain and D. Piga: An Instrumental Least Squares Support Vector Machine for System Identification, Workshop on Machine Learning for System Identification, (2013) Atlanta, Georgia, USA.
  58. Siraj, M.M. and R. Tóth: On The Problem of Model Reduction of LPV Systems, Proc. of the 31st Benelux Meeting, (2012) Heijderbos, The Netherlands, pp. 146.
  59. Dankers, A. G., R. Tóth, P. S. C. Heuberger and P. M. J. Van den Hof: Informativity of Data Sets for LPV Prediction-Error Identification, Proc. of the 30th Benelux Meeting, (2011) Lommel, Belgium, pp. 70.
  60. Tóth, R., J.C. Willems, P. S. C. Heuberger and P. M. J. Van den Hof: Extension of the behavioral approach to linear parameter-varying systems, Proc. of the 28th Benelux Meeting, (2009) Spa, Belgium, pp. 131.
  61. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: (In)equivalence of discrete time LPV state-space and input/output representations, Proc. of the 26th Benelux Meeting, (2007) Lommel, Belgium, pp. 34.
  62. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: Robust and optimal selection of OBFs based model structures, Proc. of the 25th Benelux Meeting, (2006) Heeze, The Netherlands, pp. 27.
  63. Tóth, R., P. S. C. Heuberger and P. M. J. Van den Hof: Identification of LPV systems using orthonormal basis functions, Proc. of the 24th Benelux Meeting, (2005) Houffalize, Belgium, pp. 70.

Technical Reports (13)

  1. Verhoek, C., S. Haesaert, and R. Tóth: Another note on persistency of excitation, The linear parameter-varying case, Eindhoven University of Technology, 2023.
  2. Koelewijn, P.J.W. and R. Tóth: Physical Parameter Estimation of an Unbalanced Disc System, Eindhoven University of Technology, 2019. (ERC) (pdf)
  3. Koelewijn, P.J.W. and R. Tóth: Incremental Gain of LTI Systems, Eindhoven University of Technology, 2019. (ERC) (pdf)
  4. Darwish, M. A. H., P. B. Cox, I. Proimadis, G. Pillonetto and R. Tóth: Prediction-Error Identification of LPV Systems: A Nonparametric Gaussian Regression Approach, Eindhoven University of Technology, TUE-CS-2017-001, 2016. (pdf)
  5. Cox, P. B. and R. Tóth: On the Connection Between Different Noise Structures for LPV-SS Models, Eindhoven University of Technology, TUE-CS-2016-003, 2016. (pdf)
  6. R. Tóth: IO realization of an LPV-SS form with static dependency, TUE–CS-2015-001, 2015. (pdf)
  7. Wollnack, S, A. S. Hossam, Herbert Werner, and R. Tóth: Using Implicit IO Representations for Stability Analysis and LPV-IO Controller Synthesis. Eindhoven University of Technology, TUE–CS-2014-003, 2014. (pdf)
  8. Laurain, V., R. Tóth, D. Piga: Instrumental Variables Based Least Squares Support Vector Machine for Identification of Nonlinear Systems, Eindhoven University of Technology, TUE–CS-2013-005, 2013. (pdf)
  9. Formentin, S., D. Piga, R. Tóth, and S. Savaresi. LPV control system design from data, Eindhoven University of Technology, TUE–CS-2013-004, 2013. (pdf)
  10. R. Tóth: Maximum LPV-SS realization in a static form, Eindhoven University of Technology, TUE–CS-2013-003, 2013. (pdf)
  11. Piga, D. and R. Tóth: Data-driven LPV modeling of continuous pulp digesters, Eindhoven University of Technology, TUE–CS-2013-002, 2013. (pdf)
  12. Laurain, V., R. Tóth, D. Piga: An Instrumental Least Squares Support Vector Machine for Nonlinear System Identification: enforcing zero-centering constraints, Eindhoven University of Technology, TUE–CS-2013-001, 2013. (pdf)
  13. Tóth, R., M. Lovera, P. S. C. Heuberger, M. Corno and P. M. J. Van den Hof: On the Discretization of Linear Fractional Representations of LPV Systems: Detailed Derivation of the Formulas, Tech. Report, Delft University of Technology, R-11-037, 2011. (pdf)

Lecture notes (2)

  1. Tóth, R. and A. Balogh: Collection of problems and solutions for electrical circuits, Lecture Notes in Electrical Engineering, in Hungarian, Pannon University, 2002.
  2. Fodor, D. and R. Tóth: Digital Signal Processing, Lecture Notes in Electrical Engineering, in Hungarian, Pannon University, 2002.

PhD Thesis

  1. Tóth, R.: Modeling and Identification of Linear Parameter-Varying Systems, an Orthonormal Basis Function Approach, Phd. Thesis, Delft University of Technology, 2008.