Results 31 to 40 of about 7,831,201 (331)
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning [PDF]
Proceedings of the Royal Society A, 2022 Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applications.
Kevin Zeng, Alec J. Linot, M. Graham
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Data-driven biped control [PDF]
ACM Transactions on Graphics, 2010 We present a dynamic controller to physically simulate under-actuated three-dimensional full-body biped locomotion. Our data-driven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that
Yoonsang Lee, Sungeun Kim, Jehee Lee
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Data-Driven Tests for Controllability [PDF]
IEEE Control Systems Letters, 2021 The fundamental lemma due to Willems et al. “A note on persistency of excitation,” Syst. Control Lett., vol. 54, no. 4, pp. 325–329, 2005 plays an important role in system identification and data-driven control. One of the assumptions for the fundamental lemma is that the underlying linear time-invariant system is controllable.
Mishra, Vikas Kumar +2 more
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Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller
IEEE Access, 2023 This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the
Hyuntae Kim, Hamin Chang
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Data-Driven Robust Control Using Reinforcement Learning
Applied Sciences, 2022 This paper proposes a robust control design method using reinforcement learning for controlling partially-unknown dynamical systems under uncertain conditions.
Phuong D. Ngo +2 more
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Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness
IEEE Transactions on Automatic Control, 2019 In a paper by Willems et al., it was shown that persistently exciting data can be used to represent the input–output behavior of a linear system. Based on this fundamental result, we derive a parametrization of linear feedback systems that paves the way ...
C. De Persis, P. Tesi
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Data‐driven control of dynamic event‐triggered systems with delays [PDF]
International Journal of Robust and Nonlinear Control, 2021 This article studies data‐driven control of unknown sampled‐data linear systems with communication delays under an event‐triggering transmission mechanism.
Xin Wang +5 more
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On the linear quadratic data-driven control [PDF]
, 2007 The classical approach for solving control problems is model based: first a model representation is derived from given data of the plant and then a control law is synthesized using the model and the control specifications.
Markovsky, Ivan, Rapisarda, Paolo
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SoftwareX, 2020 In this paper, the pyvrft, a Python package for the data-driven control method known as Virtual Reference Feedback Tuning (VRFT), is presented. Virtual Reference Feedback Tuning is a control design technique that does not use a mathematical model from ...
Emerson Boeira, Diego Eckhard
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A Matrix Finsler’s Lemma with Applications to Data-Driven Control [PDF]
IEEE Conference on Decision and Control, 2021 In a recent paper it was shown how a matrix S-lemma can be applied to construct controllers from noisy data. The current paper complements these results by proving a matrix version of the classical Finsler's lemma.
H. V. Waarde, M. Camlibel
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