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2020 – today
- 2025
[j58]Leontine Aarnoudse, Alexey Pavlov, Tom Oomen:
Nonlinear iterative learning control for discriminating between disturbances. Autom. 171: 111902 (2025)- 2024
- [j57]Tom Oomen, Cristian R. Rojas:
Reset-free data-driven gain estimation: Power iteration using reversed-circulant matrices. Autom. 161: 111505 (2024) - [j56]Rodrigo A. González
, Koen Tiels, Tom Oomen:
Kernel-based identification using Lebesgue-sampled data. Autom. 164: 111648 (2024) - [j55]Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh, Tom Oomen:
Statistical Analysis of Block Coordinate Descent Algorithms for Linear Continuous-Time System Identification. IEEE Control. Syst. Lett. 8: 388-393 (2024) - [j54]Johan Kon, Roland Tóth, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen:
Guaranteeing Stability in Structured Input-Output Models: With Application to System Identification. IEEE Control. Syst. Lett. 8: 1565-1570 (2024) - [j53]Rodrigo A. González, Max van Haren, Tom Oomen, Cristian R. Rojas:
Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency. IEEE Control. Syst. Lett. 8: 2415-2420 (2024) - [j52]Lars Van De Kamp, Joey Reinders, Bram Hunnekens, Tom Oomen, Nathan van de Wouw:
Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions. IFAC J. Syst. Control. 27: 100236 (2024) - [j51]Leontine Aarnoudse, Johan Kon, Wataru Ohnishi, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen:
Control-relevant neural networks for feedforward control with preview: Applied to an industrial flatbed printer. IFAC J. Syst. Control. 27: 100241 (2024) - [j50]Lars Van De Kamp, Bram Hunnekens, Nathan van de Wouw, Tom Oomen:
Improving breathing effort estimation in mechanical ventilation via optimal experiment design. IFAC J. Syst. Control. 29: 100270 (2024) - [j49]Isaac A. Spiegel, Nard Strijbosch, Robin de Rozario, Tom Oomen, Kira Barton:
Stable Inversion of Piecewise Affine Systems With Application to Feedforward and Iterative Learning Control. IEEE Trans. Autom. Control. 69(10): 6836-6851 (2024) - [j48]Maurice Poot, Max van Haren, Dragan Kostic, Jim Portegies, Tom Oomen:
Position-Dependent Motion Feedforward via Gaussian Processes: Applied to Snap and Force Ripple in Semiconductor Equipment. IEEE Trans. Control. Syst. Technol. 32(6): 1968-1982 (2024) - [j47]Tom Bloemers, Sjoerd Leemrijse, Valentin Preda, Fabrice Boquet, Tom Oomen, Roland Tóth:
Vibration Control Under Frequency-Varying Disturbances With Application to Satellites. IEEE Trans. Control. Syst. Technol. 32(6): 1983-1994 (2024) - [c112]Max van Meer, Kjell van Schie, Gert Witvoet, Tom Oomen:
Automated Model-Free Commutation for Coarse Pointing Actuators in Free-Space Optical Communication. AIM 2024: 592-597 - [c111]Kentaro Tsurumoto, Wataru Ohnishi, Takafumi Koseki, Max van Haren, Tom Oomen:
Combined Time-Domain Optimization Design for Task-Flexible and High Performance ILC. ECC 2024: 1190-1195 - [c110]Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen:
Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification. ECC 2024: 2143-2148 - [c109]Max van Meer, Gert Witvoet, Tom Oomen:
Robust Commutation Design: Applied to Switched Reluctance Motors. ECC 2024: 2448-2453 - [c108]Mathyn van Dael, Gert Witvoet, Bas Swinkels, Diego Bersanetti, Manuel Pinto, Julia Casanueva, Maddalena Mantovani, Piernicola Spinicelli, Camilla De Rossi, Tom Oomen:
Iterative interaction decoupling for multivariate time-varying systems applied to a Gravitational Wave Detector. ECC 2024: 2454-2459 - [i47]Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh, Tom Oomen:
Identification of Additive Continuous-time Systems in Open and Closed-loop. CoRR abs/2401.01263 (2024) - [i46]Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen:
Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification. CoRR abs/2401.10052 (2024) - [i45]Max van Meer, Gert Witvoet, Tom Oomen:
Robust Commutation Design: Applied to Switched Reluctance Motors. CoRR abs/2402.01216 (2024) - [i44]Rogier Dinkla, Sebastiaan Paul Mulders, Tom Oomen, Jan-Willem van Wingerden:
Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control. CoRR abs/2402.14374 (2024) - [i43]Max van Haren, Kentaro Tsurumoto, Masahiro Mae, Lennart Blanken, Wataru Ohnishi, Tom Oomen:
A Frequency-Domain Approach for Enhanced Performance and Task Flexibility in Finite-Time ILC. CoRR abs/2403.02039 (2024) - [i42]Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh, Tom Oomen:
Statistical Analysis of Block Coordinate Descent Algorithms for Linear Continuous-time System Identification. CoRR abs/2404.09071 (2024) - [i41]Isaac A. Spiegel, Nard Strijbosch, Robin de Rozario, Tom Oomen, Kira Barton:
Stable Inversion of Piecewise Affine Systems with Application to Feedforward and Iterative Learning Control. CoRR abs/2404.09845 (2024) - [i40]Rogier Dinkla, Tom Oomen, Sebastiaan Paul Mulders, Jan-Willem van Wingerden:
Data-enabled Predictive Repetitive Control. CoRR abs/2408.15210 (2024) - [i39]Rodrigo A. González, Max van Haren, Tom Oomen, Cristian R. Rojas:
Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency. CoRR abs/2410.19629 (2024) - 2023
- [j46]Max van Haren, Leonid Mirkin, Lennart Blanken, Tom Oomen:
Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems. IEEE Control. Syst. Lett. 7: 2131-2136 (2023) - [j45]Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Tom Oomen, Wojciech Paszke, Eric Rogers:
Optimal iterative learning control design for continuous-time systems with nonidentical trial lengths using alternating projections between multiple sets. J. Frankl. Inst. 360(5): 3825-3848 (2023) - [j44]Robin de Rozario, Tom Oomen:
Frequency Response Function-Based Learning Control: Analysis and Design for Finite-Time Convergence. IEEE Trans. Autom. Control. 68(3): 1807-1814 (2023) - [j43]Noud Mooren, Gert Witvoet, Tom Oomen:
Gaussian Process Repetitive Control With Application to an Industrial Substrate Carrier System With Spatial Disturbances. IEEE Trans. Control. Syst. Technol. 31(1): 344-358 (2023) - [j42]Joey Reinders, David Elshove, Bram Hunnekens, Nathan van de Wouw, Tom Oomen:
Triggered Repetitive Control: Application to Mechanically Ventilated Patients. IEEE Trans. Control. Syst. Technol. 31(4): 1581-1593 (2023) - [j41]Joey Reinders, Mattia Giaccagli, Bram Hunnekens, Daniele Astolfi, Tom Oomen, Nathan van de Wouw:
Repetitive Control for Lur'e-Type Systems: Application to Mechanical Ventilation. IEEE Trans. Control. Syst. Technol. 31(4): 1819-1829 (2023) - [j40]Nard Strijbosch, Koen Tiels, Tom Oomen:
Memory-Element-Based Hysteresis: Identification and Compensation of a Piezoelectric Actuator. IEEE Trans. Control. Syst. Technol. 31(6): 2863-2870 (2023) - [j39]Nic Dirkx, Koen Tiels, Tom Oomen:
A Wavelet-Based Approach to FRF Identification From Incomplete Data. IEEE Trans. Instrum. Meas. 72: 1-15 (2023) - [c107]Noa van Rijt, Ahmad Faza, Tom Oomen, Arnfinn Aas Eielsen:
Learning Control Applied to a Digital-to-analogue Converter. CCTA 2023: 91-96 - [c106]Rogier Dinkla, Tom Oomen, Jan-Willem van Wingerden, Sebastiaan Paul Mulders:
Data-Driven LIDAR Feedforward Predictive Wind Turbine Control. CCTA 2023: 559-565 - [c105]Nic Dirkx, Koen Tiels, Tom Oomen:
Iterative Robust Experiment Design for MIMO System Identification via the S-Lemma. CCTA 2023: 998-1003 - [c104]Hanul Jung, Paul Tacx, Tom Oomen, Sehoon Oh:
Novel Disturbance Observer Relevant Parametric System Identification Based on Robust Stability Criterion. CCTA 2023: 1010-1015 - [c103]Leontine Aarnoudse, Alexey Pavlov, Johan Kon, Tom Oomen:
Nonlinear Repetitive Control for Mitigating Noise Amplification. CDC 2023: 2891-2896 - [c102]Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen:
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach. CDC 2023: 3720-3725 - [c101]Leontine Aarnoudse, Tom Oomen:
Efficient MIMO Iterative Feedback Tuning via Randomization. CDC 2023: 4512-4517 - [c100]Maurice Poot, Jim Portegies, Dragan Kostic, Tom Oomen:
Rational Basis Functions in Iterative Learning Control for Multivariable Systems. CDC 2023: 4644-4649 - [c99]Max van Meer, Rodrigo A. González, Gert Witvoet, Tom Oomen:
Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors. CDC 2023: 5494-5499 - [c98]Lotfi M. Chaouach, Tom Oomen, Dimitris Boskos:
Comparing Structured Ambiguity Sets for Stochastic Optimization: Application to Uncertainty Quantification. CDC 2023: 8274-8279 - [c97]Robbert van der Kruk, Arend Jan van Noorden, Tom Oomen, René van de Molengraft, Herman Bruyninckx:
Robotic Control for Vibration Reduction of Swinging Products. ICM 2023: 1-8 - [i38]Rodrigo A. González, Koen Tiels, Tom Oomen:
Kernel-based identification using Lebesgue-sampled data. CoRR abs/2303.06045 (2023) - [i37]Max van Haren, Lennart Blanken, Tom Oomen:
A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems. CoRR abs/2303.07932 (2023) - [i36]Johan Kon, Naomi de Vos, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen:
Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control. CoRR abs/2303.07994 (2023) - [i35]Max van Meer, Emre Deniz, Gert Witvoet, Tom Oomen:
Cascaded Calibration of Mechatronic Systems via Bayesian Inference. CoRR abs/2304.03136 (2023) - [i34]Rodrigo A. González, Koen Tiels, Tom Oomen:
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach. CoRR abs/2304.03312 (2023) - [i33]Koen Classens, W. P. M. H. Heemels, Tom Oomen:
Direct Shaping of Minimum and Maximum Singular Values: An ℋ/ℋ∞ Synthesis Approach for Fault Detection Filters. CoRR abs/2305.07258 (2023) - [i32]Max van Haren, Leonid Mirkin, Lennart Blanken, Tom Oomen:
Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems. CoRR abs/2306.02944 (2023) - [i31]Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen:
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach. CoRR abs/2309.12722 (2023) - [i30]Max van Meer, Rodrigo A. González, Gert Witvoet, Tom Oomen:
Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors. CoRR abs/2309.17099 (2023) - 2022
- [j38]Nard Strijbosch, Tom Oomen:
Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications. Autom. 139: 110171 (2022) - [j37]Noud Mooren, Gert Witvoet, Tom Oomen:
Gaussian process repetitive control: Beyond periodic internal models through kernels. Autom. 140: 110273 (2022) - [j36]Nard Strijbosch, Koen Tiels, Tom Oomen:
Hysteresis Feedforward Compensation: A Direct Tuning Approach Using Hybrid-MEM-Elements. IEEE Control. Syst. Lett. 6: 1070-1075 (2022) - [j35]Nic Dirkx, Marcel Bosselaar, Tom Oomen:
A Fast Smoothing-Based Algorithm to Generate l∞-Norm Constrained Signals for Multivariable Experiment Design. IEEE Control. Syst. Lett. 6: 1784-1789 (2022) - [j34]Tom Bloemers, Tom Oomen, Roland Tóth:
Frequency Response Data-Driven LPV Controller Synthesis for MIMO Systems. IEEE Control. Syst. Lett. 6: 2264-2269 (2022) - [j33]Max van Meer, Valentina Breschi, Tom Oomen, Simone Formentin:
Direct data-driven design of LPV controllers with soft performance specifications. J. Frankl. Inst. 359(2): 816-836 (2022) - [j32]Enzo Evers, Bram de Jager, Tom Oomen:
Incorporating Prior Knowledge in Local Parametric Modeling for Frequency Response Measurements: Applied to Thermal/Mechanical Systems. IEEE Trans. Control. Syst. Technol. 30(1): 142-152 (2022) - [j31]Tom Bloemers, Tom Oomen, Roland Tóth:
Frequency Response Data-Based LPV Controller Synthesis Applied to a Control Moment Gyroscope. IEEE Trans. Control. Syst. Technol. 30(6): 2734-2742 (2022) - [c96]Masahiro Mae, Max van Haren, Wataru Ohnishi, Tom Oomen, Hiroshi Fujimoto:
Feedforward of Sampled-Data System for High-Precision Motion Control using Basis Functions with ZOH Differentiator. ACC 2022: 3350 - [c95]Maurice Poot, Jim Portegies, Noud Mooren, Max van Haren, Max van Meer, Tom Oomen:
Gaussian Processes for Advanced Motion Control*. ACC 2022: 3355 - [c94]Kentaro Tsurumoto, Wataru Ohnishi, Takafumi Koseki, Nard Strijbosch, Tom Oomen:
A non-causal approach for suppressing the estimation delay of state observer. ACC 2022: 3356 - [c93]Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen:
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach. ACC 2022: 4377-4382 - [c92]Max van Haren, Maurice Poot, Jim Portegies, Tom Oomen:
Position-Dependent Snap Feedforward: A Gaussian Process Framework. ACC 2022: 4778-4783 - [c91]Paul Tacx, Tom Oomen:
Bode Analysis of Uncertain Multivariable Systems. ACC 2022: 5056-5061 - [c90]Leontine Aarnoudse, Johan Kon, Koen Classens, Max van Meer, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen:
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach *. CDC 2022: 1485-1490 - [c89]Jan-Willem van Wingerden, Sebastiaan Paul Mulders, Rogier Dinkla, Tom Oomen, Michel Verhaegen:
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control. CDC 2022: 2111-2116 - [c88]Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen:
Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics. CDC 2022: 2475-2480 - [c87]Max van Haren, Lennart Blanken, Tom Oomen:
Frequency Domain Identification of Multirate Systems: A Lifted Local Polynomial Modeling Approach. CDC 2022: 2795-2800 - [c86]Lotfi M. Chaouach, Dimitris Boskos, Tom Oomen:
Uncertain uncertainty in data-driven stochastic optimization: towards structured ambiguity sets. CDC 2022: 4776-4781 - [i29]Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen:
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach. CoRR abs/2201.03308 (2022) - [i28]Merijn Floren, Koen Classens, Tom Oomen, Jean-Philippe Noël:
Data-driven feedback linearisation using model predictive control. CoRR abs/2201.04550 (2022) - [i27]Max van Haren, Maurice Poot, Dragan Kostic, Robin van Es, Jim Portegies, Tom Oomen:
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder. CoRR abs/2201.07511 (2022) - [i26]Max van Haren, Maurice Poot, Jim Portegies, Tom Oomen:
Position-Dependent Snap Feedforward: A Gaussian Process Framework. CoRR abs/2202.00257 (2022) - [i25]Johan Kon, Marcel Heertjes, Tom Oomen:
Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution. CoRR abs/2202.05337 (2022) - [i24]Max van Haren, Lennart Blanken, Tom Oomen:
Frequency Domain Identification of Multirate Systems: A Lifted Local Polynomial Modeling Approach. CoRR abs/2208.07016 (2022) - [i23]Leontine Aarnoudse, Tom Oomen:
Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation. CoRR abs/2209.05139 (2022) - [i22]Leontine Aarnoudse, Johan Kon, Koen Classens, Max van Meer, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen:
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach. CoRR abs/2209.05155 (2022) - [i21]Jan-Willem van Wingerden, Sebastiaan Paul Mulders, Rogier Dinkla, Tom Oomen, Michel Verhaegen:
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control. CoRR abs/2209.05210 (2022) - [i20]Max van Meer, Gert Witvoet, Tom Oomen:
Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression. CoRR abs/2209.06550 (2022) - [i19]Jilles van Hulst, Maurice Poot, Dragan Kostic, Kai Wa Yan, Jim Portegies, Tom Oomen:
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach. CoRR abs/2209.11504 (2022) - [i18]Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen:
Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics. CoRR abs/2209.12489 (2022) - [i17]Max van Haren, Lennart Blanken, Tom Oomen:
Basis Function feedforward for Position-Dependent Systems. CoRR abs/2211.01833 (2022) - 2021
- [j30]Robbert Voorhoeve, Tom Oomen:
Data-dependent orthogonal polynomials on generalized circles: A unified approach applied to δ-domain identification. Autom. 131: 109709 (2021) - [j29]Leontine Aarnoudse, Tom Oomen:
Model-Free Learning for Massive MIMO Systems: Stochastic Approximation Adjoint Iterative Learning Control. IEEE Control. Syst. Lett. 5(6): 1946-1951 (2021) - [j28]Joey Reinders, Bram Hunnekens, Frank Heck, Tom Oomen, Nathan van de Wouw:
Adaptive Control for Mechanical Ventilation for Improved Pressure Support. IEEE Trans. Control. Syst. Technol. 29(1): 180-193 (2021) - [j27]Robbert Voorhoeve, Robin de Rozario, Wouter H. T. M. Aangenent, Tom Oomen:
Identifying Position-Dependent Mechanical Systems: A Modal Approach Applied to a Flexible Wafer Stage. IEEE Trans. Control. Syst. Technol. 29(1): 194-206 (2021) - [c85]Maurice Poot, Jim Portegies, Tom Oomen:
Kernel-Based Learning Control for Iteration-Varying Tasks Applied to a Printer With Friction. AIM 2021: 1052-1057 - [c84]Thijs Sieswerda, Andrew J. Fleming, Tom Oomen:
Model-free Multi-variable Learning Control of a Five Axis Nanopositioning Stage. AIM 2021: 1190-1194 - [c83]Nic Dirkx, Marcel Bosselaar, Tom Oomen:
Peak Amplitude-Constrained Experiment Design for FRF Identification of MIMO Motion Systems. AMC 2021: 256-261 - [c82]Max van Haren, Maurice Poot, Dragan Kostic, Robin van Es, Jim Portegies, Tom Oomen:
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder. AMC 2021: 268-273 - [c81]Noud Mooren, Gert Witvoet, Tom Oomen:
A Gaussian Process Approach to Multiple Internal Models in Repetitive Control. AMC 2021: 274-279 - [c80]Mathyn van Dael, Gert Witvoet, Bas Swinkels, Tom Oomen:
Systematic feedback control design for scattered light noise mitigation in Virgo's MultiSAS. AMC 2021: 300-305 - [c79]Paul Tacx, Tom Oomen:
Accurate H∞iH∞-Norm Estimation via Finite-Frequency Norms of Local Parametric Models. ACC 2021: 332-337 - [c78]Stan Verbeek, Tom Oomen, Arnfinn Aas Eielsen:
Glitch Compensation for a Digital-to-Analogue Converter. ACC 2021: 751-757 - [c77]Koen Classens, W. P. M. H. Heemels, Tom Oomen:
Closed-loop Aspects in MIMO Fault Diagnosis with Application to Precision Mechatronics. ACC 2021: 1756-1761 - [c76]Leontine Aarnoudse, Tom Oomen:
Model-Free Learning for Massive MIMO Systems: Stochastic Approximation Adjoint Iterative Learning Control. ACC 2021: 2181-2186 - [c75]Nic Dirkx, Noud Mooren, Tom Oomen:
Suppressing non-collocated disturbances in inferential motion control: with application to a wafer stage. ACC 2021: 4333-4338 - [c74]Joey Reinders, Bram Hunnekens, Tom Oomen, Nathan van de Wouw:
Linear repetitive control for a nonlinear mechanical ventilation system using feedback linearization. CCTA 2021: 719-726 - [c73]Leontine Aarnoudse, Tom Oomen:
Conjugate Gradient MIMO Iterative Learning Control Using Data-Driven Stochastic Gradients. CDC 2021: 3749-3754 - [c72]Johan Kon, Nard Strijbosch, Sjirk H. Koekebakker, Tom Oomen:
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements. CDC 2021: 6566-6571 - [c71]Koen Classens, W. P. Maurice H. Heemels, Tom Oomen:
Digital Twins in Mechatronics: From Model-based Control to Predictive Maintenance. DTPI 2021: 336-339 - [c70]Leontine Aarnoudse, Wataru Ohnishi, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen:
Control- Relevant Neural Networks for Intelligent Motion Feedforward. ICM 2021: 1-6 - [c69]Koen Classens, W. P. M. H. Heemels, Tom Oomen:
A Closed-Loop Perspective on Fault Detection for Precision Motion Control: With Application to an Overactuated System. ICM 2021: 1-6 - [c68]Nic Dirkx, Tom Oomen:
Suppressing spatially distributed disturbances by exploiting additional sensors and actuators in inferential motion control. ICM 2021: 1-6 - [c67]Wataru Ohnishi, Nard Strijbosch, Tom Oomen:
Multirate State Tracking for Improving Intersample Behavior in Iterative Learning Control. ICM 2021: 1-6 - [i16]Tom Bloemers, Roland Tóth, Tom Oomen:
Frequency-Domain Data-Driven Controller Synthesis for Unstable LPV Systems. CoRR abs/2107.09712 (2021) - [i15]Isaac A. Spiegel, Nard Strijbosch, Tom Oomen, Kira Barton:
Iterative learning control with discrete-time nonlinear nonminimum phase models via stable inversion. CoRR abs/2108.07315 (2021) - [i14]Tom Bloemers, Roland Tóth, Tom Oomen:
Frequency Response Data Based LPV Controller Synthesis Applied to a Control Moment Gyroscope. CoRR abs/2109.05774 (2021) - [i13]Leontine Aarnoudse, Tom Oomen:
Conjugate gradient MIMO iterative learning control using data-driven stochastic gradients. CoRR abs/2111.08445 (2021) - [i12]Johan Kon, Nard Strijbosch, Sjirk H. Koekebakker, Tom Oomen:
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements. CoRR abs/2111.13008 (2021) - [i11]Max van Meer, Maurice Poot, Jim Portegies, Tom Oomen:
Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction. CoRR abs/2112.03805 (2021) - 2020
- [j26]Lennart Blanken, Tom Oomen:
Kernel-based identification of non-causal systems with application to inverse model control. Autom. 114: 108830 (2020) - [j25]Frank Boeren, Alexander Lanzon, Tom Oomen:
Iterative Identification and Control Using Non-normalized Coprime Factors With Application in Wafer Stage Motion Control. IEEE Trans. Control. Syst. Technol. 28(2): 413-424 (2020) - [j24]Jurgen van Zundert, Tom Oomen, Jan Verhaegh, Wouter H. T. M. Aangenent, Duarte J. Antunes, W. P. M. H. Heemels:
Beyond Performance/Cost Tradeoffs in Motion Control: A Multirate Feedforward Design With Application to a Dual-Stage Wafer System. IEEE Trans. Control. Syst. Technol. 28(2): 448-461 (2020) - [j23]Lennart Blanken, Tom Oomen:
Multivariable Iterative Learning Control Design Procedures: From Decentralized to Centralized, Illustrated on an Industrial Printer. IEEE Trans. Control. Syst. Technol. 28(4): 1534-1541 (2020) - [c66]Tom Oomen:
Learning for Advanced Motion Control. AMC 2020: 65-72 - [c65]Enzo Evers, Robbert Voorhoeve, Tom Oomen:
On Frequency Response Function Identification for Advanced Motion Control. AMC 2020: 319-324 - [c64]Noud Mooren, Gert Witvoet, Ibrahim Açan, Joep Kooijman, Tom Oomen:
Suppressing Position-Dependent Disturbances in Repetitive Control: With Application to a Substrate Carrier System. AMC 2020: 331-336 - [c63]Nard Strijbosch, Tom Oomen:
Hybrid-MEM-Element Feedforward: With Application to Hysteretic Piezoelectric Actuators. CDC 2020: 934-939 - [i10]Joey Reinders, Ruben Verkade, Bram Hunnekens, Nathan van de Wouw, Tom Oomen:
Improving mechanical ventilation for patient care through repetitive control. CoRR abs/2004.00312 (2020) - [i9]Tom Oomen:
Learning for Advanced Motion Control. CoRR abs/2004.11017 (2020) - [i8]Enzo Evers, Robbert Voorhoeve, Tom Oomen:
On Frequency Response Function Identification for Advanced Motion Control. CoRR abs/2006.10373 (2020) - [i7]Enzo Evers, Rens Slenders, Rob W. van Gils, Tom Oomen:
Temperature-Dependent Modeling of Thermoelectric Elements. CoRR abs/2006.10379 (2020) - [i6]Leontine Aarnoudse, Nard Strijbosch, Edwin Verschueren, Tom Oomen:
Commutation-Angle Iterative Learning Control for Intermittent Data: Enhancing Piezo-Stepper Actuator Waveforms. CoRR abs/2006.13572 (2020) - [i5]Noud Mooren, Gert Witvoet, Tom Oomen:
Gaussian Process Repetitive Control for Suppressing Spatial Disturbances. CoRR abs/2006.16719 (2020) - [i4]Maurice Poot, Jim Portegies, Tom Oomen:
On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control. CoRR abs/2007.00430 (2020)
2010 – 2019
- 2019
- [j22]Robin de Rozario, Tom Oomen:
Data-driven iterative inversion-based control: Achieving robustness through nonlinear learning. Autom. 107: 342-352 (2019) - [j21]Jurgen van Zundert, Tom Oomen:
Stable inversion of LPTV systems with application in position-dependent and non-equidistantly sampled systems. Int. J. Control 92(5): 1022-1032 (2019) - [j20]Thijs Vromen, Cam-Hing Dai, Nathan van de Wouw, Tom Oomen, Patricia Astrid, Apostolos Doris, Henk Nijmeijer:
Mitigation of Torsional Vibrations in Drilling Systems: A Robust Control Approach. IEEE Trans. Control. Syst. Technol. 27(1): 249-265 (2019) - [c62]Noud Mooren, Gert Witvoet, Tom Oomen:
Feedforward Motion Control: From Batch-to-Batch Learning to Online Parameter Estimation. ACC 2019: 947-952 - [c61]Robin de Rozario, Juliana Langen, Tom Oomen:
Multivariable Learning Using Frequency Response Data: A Robust Iterative Inversion-Based Control Approach with Application. ACC 2019: 2215-2220 - [c60]Gert Witvoet, Joost Peters, Stefan Kuiper, Tom Oomen:
Line-to-line repetitive control of a 6-DoF hexapod stage for overlay measurements using Atomic Force Microscopy. ACC 2019: 2464-2469 - [c59]Nard Strijbosch, Tom Oomen:
Beyond Quantization in Iterative Learning Control: Exploiting Time-Varying Time-Stamps. ACC 2019: 2984-2989 - [c58]Jurgen van Zundert, Wataru Ohnishi, Hiroshi Fujimoto, Tom Oomen:
System Inversion for Sampled-Data Feedforward Control: Balancing On-Sample and Intersample Behavior. ACC 2019: 4472-4477 - [c57]Joey Reinders, Frank Heck, Bram Hunnekens, Tom Oomen, Nathan van de Wouw:
Online hose calibration for pressure control in mechanical ventilation. ACC 2019: 5414-5419 - [c56]Tom Bloemers, Roland Tóth, Tom Oomen:
Towards Data-Driven LPV Controller Synthesis Based on Frequency Response Functions. CDC 2019: 5680-5685 - [c55]Nard Strijbosch, Tom Oomen:
Intermittent Sampling in Iterative Learning Control: a Monotonically-Convergent Gradient-Descent Approach with Application to Time Stamping. CDC 2019: 6542-6547 - [c54]Martin Goubej, Sven Meeusen, Noud Mooren, Tom Oomen:
Iterative learning control in high-performance motion systems: from theory to implementation. ETFA 2019: 851-856 - 2018
- [c53]Robin de Rozario, Tom Oomen:
Improving transient learning behavior in model-free inversion-based iterative control with application to a desktop printer. AMC 2018: 455-460 - [c52]Lennart Blanken, Ids van den Meijdenberg, Tom Oomen:
Kernel-based regression of non-causal systems for inverse model feedforward estimation. AMC 2018: 461-466 - [c51]Jurgen van Zundert, Tom Oomen:
LPTV loop-shaping with application to non-equidistantly sampled precision mechatronics. AMC 2018: 467-472 - [c50]Tom Oomen, Cristian R. Rojas:
Sparse iterative learning control (SPILC): When to sample for resource-efficiency? AMC 2018: 497-503 - [c49]Robin de Rozario, Remy Pelzer, Sjirk H. Koekcbakker, Tom Oomen:
Accommodating Trial-Varying Tasks in Iterative Learning Control for LPV Systems, Applied to Printer Sheet Positioning. ACC 2018: 5213-5218 - [c48]Jurgen van Zundert, Fons Luijten, Tom Oomen:
Achieving Perfect Causal Feedforward Control in Presence of Nonminimum-Phase Behavior - Exploiting Additional Actuators and Squaring Down. ACC 2018: 6031-6036 - [c47]Lennart Blanken, Goksan Isil, Sjirk H. Koekebakker, Tom Oomen:
Data-Driven Feedforward Learning using Non-Causal Rational Basis Functions: Application to an Industrial Flatbed Printer. ACC 2018: 6672-6677 - [c46]Robbert Voorhoeve, Tom Oomen:
Numerically Reliable Identification of Fast Sampled Systems: A Novel δ-Domain Data-Dependent Orthonormal Polynomial Approach. CDC 2018: 1433-1438 - [c45]Enzo Evers, Bram De Jager, Tom Oomen:
Thermo-Mechanical Behavior in Precision Motion Control: Unified Framework for Fast and Accurate FRF Identification. IECON 2018: 4618-4623 - [i3]Lennart Blanken, Tom Oomen:
Multivariable Iterative Learning Control Design Procedures: from Decentralized to Centralized, Illustrated on an Industrial Printer. CoRR abs/1806.08550 (2018) - [i2]Robbert Voorhoeve, Robin de Rozario, Wouter H. T. M. Aangenent, Tom Oomen:
Identifying Position-Dependent Mechanical Systems: A Modal Approach with Applications to Wafer Stage Control. CoRR abs/1807.06942 (2018) - 2017
- [j19]Frank Boeren, Dennis Bruijnen, Tom Oomen:
Enhancing feedforward controller tuning via instrumental variables: with application to nanopositioning. Int. J. Control 90(4): 746-764 (2017) - [j18]Rick van der Maas, Annemiek van der Maas, Robbert Voorhoeve, Tom Oomen:
Accurate FRF Identification of LPV Systems: nD-LPM With Application to a Medical X-Ray System. IEEE Trans. Control. Syst. Technol. 25(5): 1724-1735 (2017) - [j17]Joost Bolder, Jurgen van Zundert, Sjirk H. Koekebakker, Tom Oomen:
Enhancing Flatbed Printer Accuracy and Throughput: Optimal Rational Feedforward Controller Tuning Via Iterative Learning Control. IEEE Trans. Ind. Electron. 64(5): 4207-4216 (2017) - [c44]Yijie Guo, Joost Peters, Tom Oomen, Sandipan Mishra:
Distributed model predictive control for ink-jet 3D printing. AIM 2017: 436-441 - [c43]Michiel A. Beijen, Marcel Heertjes, Robbert Voorhoeve, Tom Oomen:
Evaluating performance of multivariable vibration isolators: A frequency domain identification approach applied to an industrial AVIS. ACC 2017: 3512-3517 - [c42]Robin de Rozario, Tom Oomen, Maarten Steinbuch:
Iterative Learning Control and feedforward for LPV systems: Applied to a position-dependent motion system. ACC 2017: 3518-3523 - [c41]Jurgen van Zundert, Tom Oomen:
An approach to stable inversion of LPTV systems with application to a position-dependent motion system. ACC 2017: 4890-4895 - [c40]Gianmarco Rallo, Simone Formentin, Cristian R. Rojas, Tom Oomen, Sergio M. Savaresi:
Data-driven H∞-norm estimation via expert advice. CDC 2017: 1560-1565 - [c39]Lennart Blanken, Tim Hazelaar, Sjirk H. Koekebakker, Tom Oomen:
Multivariable repetitive control design framework applied to flatbed printing with continuous media flow. CDC 2017: 4727-4732 - [i1]Tom Oomen, Cristian R. Rojas:
Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility. CoRR abs/1706.01647 (2017) - 2016
- [j16]Jurgen van Zundert, Joost Bolder, Tom Oomen:
Optimality and flexibility in Iterative Learning Control for varying tasks. Autom. 67: 295-302 (2016) - [j15]Joost Bolder, Tom Oomen:
Inferential Iterative Learning Control: A 2D-system approach. Autom. 71: 247-253 (2016) - [j14]Robbert van Herpen, Okko H. Bosgra, Tom Oomen:
Bi-Orthonormal Polynomial Basis Function Framework With Applications in System Identification. IEEE Trans. Autom. Control. 61(11): 3285-3300 (2016) - [j13]Marcel François Heertjes, Bart Van der Velden, Tom Oomen:
Constrained Iterative Feedback Tuning for Robust Control of a Wafer Stage System. IEEE Trans. Control. Syst. Technol. 24(1): 56-66 (2016) - [c38]Lennart Blanken, Frank Boeren, Dennis Bruijnen, Tom Oomen:
Rational iterative feedforward tuning: Approaches, stable inversion, and experimental comparison. ACC 2016: 2629-2634 - [c37]Jurgen van Zundert, Tom Oomen, Dip Goswami, W. P. M. H. Heemels:
On the potential of lifted domain feedforward controllers with a periodic sampling sequence. ACC 2016: 4227-4232 - [c36]Annemiek van der Maas, Rick van der Maas, Robbert Voorhoeve, Tom Oomen:
Frequency response function identification of LPV systems: A 2D-LRM approach with application to a medical X-ray system. ACC 2016: 4598-4603 - [c35]Robbert Voorhoeve, Robin de Rozario, Tom Oomen:
Identification for motion control: Incorporating constraints and numerical considerations. ACC 2016: 6209-6214 - [c34]Lennart Blanken, Sjirk H. Koekebakker, Tom Oomen:
Design and modeling aspects in multivariable iterative learning control. CDC 2016: 5502-5507 - 2015
- [j12]Joost Bolder, Tom Oomen:
Rational Basis Functions in Iterative Learning Control - With Experimental Verification on a Motion System. IEEE Trans. Control. Syst. Technol. 23(2): 722-729 (2015) - [j11]Tom Oomen, Erik Grassens, Ferdinand Hendriks:
Inferential Motion Control: Identification and Robust Control Framework for Positioning an Unmeasurable Point of Interest. IEEE Trans. Control. Syst. Technol. 23(4): 1602-1610 (2015) - [c33]J. C. D. van Zundert, J. L. C. Verhaegh, Wouter H. T. M. Aangenent, Tom Oomen, Duarte Antunes, W. P. M. H. Heemels:
Feedforward for multi-rate motion control: Enhanced performance and cost-effectiveness. ACC 2015: 2831-2836 - [c32]Joost Bolder, Tom Oomen:
Data-driven optimal ILC for multivariable systems: Removing the need for L and Q filter design. ACC 2015: 3546-3551 - [c31]Jurgen van Zundert, Joost Bolder, Tom Oomen:
Iterative Learning Control for varying tasks: Achieving optimality for rational basis functions. ACC 2015: 3570-3575 - [c30]Rick van der Maas, Annemiek van der Maas, Tom Oomen:
Accurate frequency response function identification of LPV systems: A 2D local parametric modeling approach. CDC 2015: 1465-1470 - [c29]Federico Felici, Tom Oomen:
Enhancing current density profile control in tokamak experiments using iterative learning control. CDC 2015: 5370-5377 - [c28]Frank Boeren, Lennart Blanken, Dennis Bruijnen, Tom Oomen:
Optimal estimation of rational feedforward controllers: An instrumental variable approach. CDC 2015: 6058-6063 - [c27]Frank Boeren, Abhishek Bareja, Tom Kok, Tom Oomen:
Unified ILC framework for repeating and varying tasks: A frequency domain approach with application to a wire-bonder. CDC 2015: 6724-6729 - 2014
- [j10]Robbert van Herpen, Tom Oomen, Maarten Steinbuch:
Optimally conditioned instrumental variable approach for frequency-domain system identification. Autom. 50(9): 2281-2293 (2014) - [j9]Tom Oomen:
Controlling aliased dynamics in motion systems? An identification for sampled-data control approach. Int. J. Control 87(7): 1406-1422 (2014) - [j8]Tom Oomen, Robbert van Herpen, Sander Quist, Marc M. J. van de Wal, Okko H. Bosgra, Maarten Steinbuch:
Connecting System Identification and Robust Control for Next-Generation Motion Control of a Wafer Stage. IEEE Trans. Control. Syst. Technol. 22(1): 102-118 (2014) - [j7]Tom Oomen, Rick van der Maas, Cristian R. Rojas, Håkan Hjalmarsson:
Iterative Data-Driven ℋ∞ Norm Estimation of Multivariable Systems With Application to Robust Active Vibration Isolation. IEEE Trans. Control. Syst. Technol. 22(6): 2247-2260 (2014) - [c26]Robbert van Herpen, Tom Oomen, Edward Kikken, Marc M. J. van de Wal, Wouter H. T. M. Aangenent, Maarten Steinbuch:
Exploiting additional actuators and sensors for nano-positioning robust motion control. ACC 2014: 984-990 - [c25]Joost Bolder, Tom Oomen, Maarten Steinbuch:
On inferential Iterative Learning Control: With example to a printing system. ACC 2014: 1827-1832 - [c24]Frank Boeren, Tom Oomen, Maarten Steinbuch:
Accuracy aspects in motion feedforward tuning. ACC 2014: 2178-2183 - [c23]Sachin Tejwant Navalkar, Jan-Willem van Wingerden, Edwin van Solingen, Tom Oomen, G. A. M. van Kuik:
Subspace Predictive Repetitive Control for wind turbine load alleviation using trailing edge flaps. ACC 2014: 4422-4427 - [c22]Joost Bolder, Tom Oomen, Maarten Steinbuch:
Aspects in inferential Iterative Learning Control: A 2D systems analysis. CDC 2014: 3584-3589 - 2013
- [c21]Frank Boeren, Robbert van Herpen, Tom Oomen, Marc M. J. van de Wal, Okko H. Bosgra:
Enhancing performance through multivariable weighting function design in ℋ- loop-shaping: with application to a motion system. ACC 2013: 6039-6044 - [c20]Tom Oomen, Rick van der Maas, Cristian R. Rojas, Håkan Hjalmarsson:
Iteratively learning the ℌ∞-norm of multivariable systems applied to model-error-modeling of a vibration isolation system. ACC 2013: 6703-6708 - [c19]Frank Boeren, Tom Oomen:
Iterative feedforward control: a closed-loop identification problem and a solution. CDC 2013: 6694-6699 - [c18]Joost Bolder, Tom Oomen, Maarten Steinbuch:
Exploiting rational basis functions in iterative learning control. CDC 2013: 7321-7326 - 2012
- [j6]Tom Oomen, Okko H. Bosgra:
System identification for achieving robust performance. Autom. 48(9): 1975-1987 (2012) - [j5]David Rijlaarsdam, Tom Oomen, Pieter Nuij, Johan Schoukens, Maarten Steinbuch:
Uniquely connecting frequency domain representations of given order polynomial Wiener-Hammerstein systems. Autom. 48(9): 2381-2384 (2012) - [j4]Cristian R. Rojas, Tom Oomen, Håkan Hjalmarsson, Bo Wahlberg:
Analyzing iterations in identification with application to nonparametric H∞-norm estimation. Autom. 48(11): 2776-2790 (2012) - [c17]Joost Bolder, Bas P. Lemmen, Sjirk H. Koekebakker, Tom Oomen, Okko H. Bosgra, Maarten Steinbuch:
Iterative learning control with basis functions for media positioning in scanning inkjet printers. ISIC 2012: 1255-1260 - [c16]Tom Oomen, Robbert van Herpen, Sander Quist, Marc M. J. van de Wal, Okko H. Bosgra, Maarten Steinbuch:
Next-generation wafer stage motion control: Connecting system identification and robust control. ACC 2012: 2455-2460 - [c15]Robbert van Herpen, Tom Oomen, Okko H. Bosgra:
Bi-orthonormal basis functions for improved frequency-domain system identification. CDC 2012: 3451-3456 - 2011
- [j3]Tom Oomen, Jeroen van de Wijdeven, Okko H. Bosgra:
System Identification and Low-Order Optimal Control of Intersample Behavior in ILC. IEEE Trans. Autom. Control. 56(11): 2734-2739 (2011) - [c14]Robbert van Herpen, Tom Oomen, Okko H. Bosgra:
A robust-control-relevant perspective on model order selection. ACC 2011: 1224-1229 - [c13]Tom Oomen, Erik Grassens, Ferdinand Hendriks, Robbert van Herpen, Okko H. Bosgra:
Inferential motion control: Identification and robust control with unmeasured performance variables. CDC/ECC 2011: 964-969 - 2010
- [c12]Robbert van Herpen, Tom Oomen, Marc M. J. van de Wal, Okko H. Bosgra:
Experimental evaluation of robust-control-relevance: A confrontation with a next-generation wafer stage. ACC 2010: 3493-3499 - [c11]Tom Oomen, Stan van der Meulen, Okko H. Bosgra, Maarten Steinbuch, Jos Elfring:
A robust-control-relevant model validation approach for continuously variable transmission control. ACC 2010: 3518-3523 - [c10]Willem-Jan Evers, Igo Besselink, Arjan Teerhuis, Tom Oomen, Henk Nijmeijer:
Experimental validation of a truck roll model using asynchronous measurements with low signal-to-noise ratios. ACC 2010: 4588-4593 - [c9]Tom Oomen, Sander Quist, Robbert van Herpen, Okko H. Bosgra:
Identification and visualization of robust-control-relevant model sets with application to an industrial wafer stage. CDC 2010: 5530-5535
2000 – 2009
- 2009
- [j2]Tom Oomen, Jeroen van de Wijdeven, Okko H. Bosgra:
Suppressing intersample behavior in iterative learning control. Autom. 45(4): 981-988 (2009) - [c8]Maarten Steinbuch, Kees van Berkel, George A. L. Leenknegt, Tom Oomen, Jeroen van de Wijdeven:
Reading of cracked optical discs using Iterative Learning Control. ACC 2009: 258-263 - [c7]Tom Oomen, Jeroen van de Wijdeven, Okko H. Bosgra:
Low-order system identification and optimal control of intersample behavior in ILC. ACC 2009: 271-276 - [c6]Tom Oomen, Okko H. Bosgra, Marc M. J. van de Wal:
Identification for robust inferential control. CDC 2009: 2581-2586 - 2008
- [c5]Tom Oomen, Okko H. Bosgra:
Robust-control-relevant coprime factor identification: A numerically reliable frequency domain approach. ACC 2008: 625-631 - [c4]Tom Oomen, Jeroen van de Wijdeven, Okko H. Bosgra:
Suppressing intersample behavior in Iterative Learning Control. CDC 2008: 2391-2397 - [c3]Tom Oomen, Okko H. Bosgra:
Estimating disturbances and model uncertainty in model validation for robust control. CDC 2008: 5513-5518 - 2007
- [j1]Tom Oomen, Marc M. J. van de Wal, Okko H. Bosgra:
Design framework for high-performance optimal sampled-data control with application to a wafer stage. Int. J. Control 80(6): 919-934 (2007) - [c2]Tom Oomen, Marc M. J. van de Wal, Okko H. Bosgra:
Aliasing of Resonance Phenomena in Sampled-Data Feedback Control Design: Hazards, Modeling, and a Solution. ACC 2007: 2881-2886 - 2006
- [c1]Tom Oomen, Marc M. J. van de Wal, Okko H. Bosgra:
Exploiting H∞ Sampled-Data Control Theory for High-Precision Electromechanical Servo Control Design. ACC 2006: 1086-1091
Coauthor Index
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