Online policy iterative-based H∞ optimization algorithm for a class of nonlinear systems

Abstract 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.