A novel optimal control design for unknown nonlinear systems based on adaptive dynamic programming and nonlinear model predictive control

This paper presents a novel adaptive optimal control algorithm by combining adaptive dynamic programming with nonlinear model predictive control for unknown continuous-time affine nonlinear systems. The adaptive optimal control design is realized by the model-critic-actor architecture. Model neural network, critic neural network and actor neural network are constructed to approximate the system dynamics, the cost function and the optimal control law respectively. The random initialization of neural networks usually influences the control performance, so three neural networks are initialized properly to obtain the suitable initial values so that the control performance is improved. Especially, actor neural network is initialized to approximate the near-optimal control law which is obtained from nonlinear model predictive control. The convergence of the proposed algorithm is proved by the Lyapunov theory. Finally, simulation results are provided to illustrate the effectiveness of the proposed algorithm.