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ORCIDNard Strijbosch
Person information
- affiliation: Eindhoven University of Technology, Departement of Mechanical Engineering, Netherlands
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2020 – today
- 2023
[j4]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)- [i5]Max Bolderman, Hans Butler, Sjirk H. Koekebakker, Eelco van Horssen, Ramidin Kamidi, Theresa Spaan-Burke, Nard Strijbosch, Mircea Lazar:
Physics-guided neural networks for feedforward control with input-to-state stability guarantees. CoRR abs/2301.08568 (2023) - 2022
- [j3]Nard Strijbosch, Tom Oomen:
Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications. Autom. 139: 110171 (2022) - [j2]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) - [c9]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 - [c8]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 - [i4]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) - 2021
- [j1]Nard Strijbosch, Geir E. Dullerud, Andrew R. Teel, W. P. M. H. Heemels:
$\mathcal {L}_2$-Gain Analysis of Periodic Event-Triggered Control and Self-Triggered Control Using Lifting. IEEE Trans. Autom. Control. 66(8): 3749-3756 (2021) - [c7]Johan Kon, Nard Strijbosch, Sjirk H. Koekebakker, Tom Oomen:
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements. CDC 2021: 6566-6571 - [c6]Leontine Aarnoudse, Wataru Ohnishi, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen:
Control- Relevant Neural Networks for Intelligent Motion Feedforward. ICM 2021: 1-6 - [c5]Wataru Ohnishi, Nard Strijbosch, Tom Oomen:
Multirate State Tracking for Improving Intersample Behavior in Iterative Learning Control. ICM 2021: 1-6 - [i3]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) - [i2]Johan Kon, Nard Strijbosch, Sjirk H. Koekebakker, Tom Oomen:
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements. CoRR abs/2111.13008 (2021) - 2020
- [c4]Nard Strijbosch, Tom Oomen:
Hybrid-MEM-Element Feedforward: With Application to Hysteretic Piezoelectric Actuators. CDC 2020: 934-939 - [i1]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)
2010 – 2019
- 2019
- [c3]Nard Strijbosch, Tom Oomen:
Beyond Quantization in Iterative Learning Control: Exploiting Time-Varying Time-Stamps. ACC 2019: 2984-2989 - [c2]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 - 2017
- [c1]N. W. A. Strijbosch, Geir E. Dullerud, Andrew R. Teel, W. P. Maurice H. Heemels:
L2-gain analysis of periodic event-triggered systems with varying delays using lifting techniques. CDC 2017: 6069-6075
Coauthor Index
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