Approximate Optimal Control of Nonlinear Systems with Mismatched Perturbations Based on Asymptotically Stable Critic Neural Network
Authors: 
Bo Zhao, Jiaxu Hou, Ping Guo, Derong Liu, Yancai XuAuthors Info & Claims
Bo Zhao, Jiaxu Hou, Ping Guo, Derong Liu, Yancai XuAuthors Info & ClaimsPages 60 - 64
Abstract
In this paper, the approximate optimal control problem for nonlinear systems with mismatched perturbations is addressed through asymptotically stable critic neural network (NN). By employing the estimated perturbation via nonlinear perturbation observer, the online updated value function is constructed to reflect the real-time perturbations, regulation and control simultaneously. In order to solve the Hamilton-Jacobi-Bellman equation, an asymptotically stable critic NN is established based on the novel nested update laws. Thus, the approximate optimal control is obtained to guarantee the closed-loop system to be uniformly ultimately bounded based on the Lyapunov's direct method. Simulation results illustrate the effectiveness of the developed control scheme.
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Index Terms
- Approximate Optimal Control of Nonlinear Systems with Mismatched Perturbations Based on Asymptotically Stable Critic Neural Network
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Published In
January 2020
279 pages
ISBN:9781450376730
DOI:10.1145/3379247
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Association for Computing Machinery
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Published: 07 March 2020
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- Research-article
- Research
- Refereed limited
Funding Sources
- State Key Laboratory of Synthetical Automation for Process Industries
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- Early Career Development Award of SKLMCCS
Conference
ICCDE 2020
ICCDE 2020: 2020 The 6th International Conference on Computing and Data Engineering
January 4 - 6, 2020
Sanya, China
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