Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach
Shuping He, Haiyang Fang, Maoguang Zhang, Fei Liu, Zhengtao Ding · IEEE Transactions on Neural Networks and Learning Systems · 2020
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.