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2020 – today
- 2022
[j37]Nard Strijbosch, Tom Oomen:
Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications. Autom. 139: 110171 (2022)- [j36]Noud Mooren, Gert Witvoet, Tom Oomen:
Gaussian process repetitive control: Beyond periodic internal models through kernels. Autom. 140: 110273 (2022) - [j35]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) - [j34]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) - [j33]Tom Bloemers, Tom Oomen, Roland Tóth:
Frequency Response Data-Driven LPV Controller Synthesis for MIMO Systems. IEEE Control. Syst. Lett. 6: 2264-2269 (2022) - [j32]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) - [j31]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) - [i21]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) - [i20]Merijn Floren, Koen Classens, Tom Oomen, Jean-Philippe Noël:
Data-driven feedback linearisation using model predictive control. CoRR abs/2201.04550 (2022) - [i19]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) - [i18]Max van Haren, Maurice Poot, Jim Portegies, Tom Oomen:
Position-Dependent Snap Feedforward: A Gaussian Process Framework. CoRR abs/2202.00257 (2022) - [i17]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) - 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) - [c84]Maurice Poot, Jim Portegies, Tom Oomen:
Kernel-Based Learning Control for Iteration-Varying Tasks Applied to a Printer With Friction. AIM 2021: 1052-1057 - [c83]Thijs Sieswerda, Andrew J. Fleming, Tom Oomen:
Model-free Multi-variable Learning Control of a Five Axis Nanopositioning Stage. AIM 2021: 1190-1194 - [c82]Nic Dirkx, Marcel Bosselaar, Tom Oomen:
Peak Amplitude-Constrained Experiment Design for FRF Identification of MIMO Motion Systems. AMC 2021: 256-261 - [c81]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 - [c80]Noud Mooren, Gert Witvoet, Tom Oomen:
A Gaussian Process Approach to Multiple Internal Models in Repetitive Control. AMC 2021: 274-279 - [c79]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 - [c78]Paul Tacx, Tom Oomen:
Accurate $\mathcal{H}_{\infty}$-Norm Estimation via Finite-Frequency Norms of Local Parametric Models. ACC 2021: 332-337 - [c77]Stan Verbeek, Tom Oomen, Arnfinn Aas Eielsen:
Glitch Compensation for a Digital-to-Analogue Converter. ACC 2021: 751-757 - [c76]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 - [c75]Leontine Aarnoudse, Tom Oomen:
Model-Free Learning for Massive MIMO Systems: Stochastic Approximation Adjoint Iterative Learning Control. ACC 2021: 2181-2186 - [c74]Nic Dirkx, Noud Mooren, Tom Oomen:
Suppressing non-collocated disturbances in inferential motion control: with application to a wafer stage. ACC 2021: 4333-4338 - [c73]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 - [c72]Leontine Aarnoudse, Tom Oomen:
Conjugate Gradient MIMO Iterative Learning Control Using Data-Driven Stochastic Gradients. CDC 2021: 3749-3754 - [c71]Johan Kon, Nard Strijbosch, Sjirk H. Koekebakker, Tom Oomen:
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements. CDC 2021: 6566-6571 - [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]