Intelligent Optimal Control of Constrained Nonlinear Systems via Receding-Horizon Heuristic Dynamic Programming

Abstract

For addressing the approximate optimal control problem of nonlinear affine systems with the terminal state constraint and asymmetric control constraints, the constrained receding-horizon heuristic dynamic programming (RH-HDP) algorithm is established in this article. In consideration of the RH mechanism of model predictive control (MPC), the approximate optimal control problem based on the HDP algorithm is transformed into a battery of subproblems. Then, the terminal state constraint related with the current prediction horizon is considered such that the terminal state is forced into the neighborhood of the system equilibrium point. In addition, the asymmetric control constraints are introduced to release the pressure of actuator saturation, so that the control input is well confined within the given constraint range. Meanwhile, relevant results of the stability proof are also displayed based on the Lyapunov approach. Finally, the constrained RH-HDP algorithm has been applied in two kinds of systems to verify its effectiveness. Comparative experiments with the traditional HDP algorithm have been carried out to verify the superiority of the present algorithm.