1. Online policy iterative-based H∞ optimization algorithm for a class of nonlinear systems.
- Author
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He, Shuping, Fang, Haiyang, Zhang, Maoguang, Liu, Fei, Luan, Xiaoli, and Ding, Zhengdao
- Subjects
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MATHEMATICAL optimization , *ALGORITHMS , *NONLINEAR systems , *RICCATI equation , *OPTIMAL control theory , *ARTIFICIAL neural networks - Abstract
• A new policy iterative algorithm is studied to design the online H ∞ optimization problems for a class of nonlinear systems. • Without considering knowledge regarding the system dynamics, the PI algorithm is derived to design the relevant H ∞ optimal control law by means of policy evaluation and policy improvement related to the corresponding algebraic Riccati equations. • It also proves the convergence of the novel PI algorithm and two simulation results are given to demonstrate the feasibility and the applicability of the designed algorithms. A novel policy iterative scheme for the design of online H ∞ optimal laws for a class of nonlinear systems is presented. First, neural network-based linear differential inclusion techniques with two multi-layered perceptions are applied to linearize the nonlinear terms. Then, an online partially model-free policy iterative scheme is applied to the linearized system to obtain the design the H ∞ optimal control law. The iterative scheme for the linear H ∞ control problem consists of policy evaluation and policy improvement by means of algebraic Riccati equations. We establish the convergence of the novel policy iterative scheme to the optimal control law. Numerical simulations demonstrating the feasibility and applicability of our design algorithm are provided. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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