Neural Network-Based ADP Cotrol for Nonliear Systems with Prescribed Performance Constriant

In this paper, the trajectory tracking control problem of nonlinear system with prescribed performance constraint was discussed. adaptive dynamic programming (ADP) is investigated to solve the problem. By introducing the constraint transformation, which is used to convert the constrained system into unconstrained one, and prescribed performance function (PPF), the steady and transient performance of closed-loop system are guaranteed. After obtained the unconstrained system, a critic network is proposed to approximate the solution of Hamilton-Jacobi-Bellman (HJB) equation. Then an optimal control was developed. Throughout the Lyapunov theory, the update laws of critic network was obtained and the stability of closed loop control system was proved. Finally, a simulation experiment was carried out to validate the effectiveness of the proposed method.