Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 2, Pages 549-558Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2905715
Keywords
Adaptive optimal control; algebraic Riccati equation (ARE); linear differential inclusion (LDI); nonlinear systems; policy iteration (PI)
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Funding
- National Natural Science Foundation of China [61673001, 61722306]
- Foundation for Distinguished Young Scholars of Anhui Province [1608085J05]
- Key Support Program of University Outstanding Youth Talent of Anhui Province [gxydZD2017001]
- State Key Program of National Natural Science Foundation of China [61833007]
- 111 Project [B12018]
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This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method.
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