Reinforcement Learning-Based Optimal Stabilization for Unknown Nonlinear Systems Subject to Inputs With Uncertain Constraints

This article presents a novel reinforcement learning strategy that addresses an optimal stabilizing problem for unknown nonlinear systems subject to uncertain input constraints. The control algorithm is composed of two parts, i.e., online learning optimal control for the nominal system and feedforwa...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 31; H. 10; S. 4330 - 4340
Hauptverfasser:	Zhao, Bo, Liu, Derong, Luo, Chaomin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Actuators Adaptive dynamic programming (ADP) Algorithms Artificial neural networks Control algorithms Control theory Distance learning Feedback control Feedforward control Feedforward systems Learning Machine learning Neural networks neural networks (NNs) Nonlinear systems Observers Optimal control Reinforcement reinforcement learning (RL) Saturation Stability analysis System dynamics uncertain input constraints unknown nonlinear systems
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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