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Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input
Liu Y.-J.3; Li S.3; Tong S.3; Chen C.L.P.1
2019
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN21622388 2162237X
Volume30Issue:1Pages:295-305
AbstractIn this paper, an optimal control algorithm is designed for uncertain nonlinear systems in discrete-time, which are in nonaffine form and with unknown dead-zone. The main contributions of this paper are that an optimal control algorithm is for the first time framed in this paper for nonlinear systems with nonaffine dead-zone, and the adaptive parameter law for dead-zone is calculated by using the gradient rules. The mean value theory is employed to deal with the nonaffine dead-zone input and the implicit function theory based on reinforcement learning is appropriately introduced to find an unknown ideal controller which is approximated by using the action network. Other neural networks are taken as the critic networks to approximate the strategic utility functions. Based on the Lyapunov stability analysis theory, we can prove the stability of systems, i.e., the optimal control laws can guarantee that all the signals in the closed-loop system are bounded and the tracking errors are converged to a small compact set. Finally, two simulation examples demonstrate the effectiveness of the design algorithm.
KeywordDiscrete-time systems neural networks (NNs) nonlinear systems optimal control reinforcement learning
DOI10.1109/TNNLS.2018.2844165
URLView the original
Language英語
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Institute of Automation Chinese Academy of Sciences
3.Liaoning University of Technology
4.Dalian Maritime University
Recommended Citation
GB/T 7714
Liu Y.-J.,Li S.,Tong S.,et al. Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input[J]. IEEE Transactions on Neural Networks and Learning Systems,2019,30(1):295-305.
APA Liu Y.-J.,Li S.,Tong S.,&Chen C.L.P..(2019).Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input.IEEE Transactions on Neural Networks and Learning Systems,30(1),295-305.
MLA Liu Y.-J.,et al."Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input".IEEE Transactions on Neural Networks and Learning Systems 30.1(2019):295-305.
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