Finite-horizon neural optimal tracking control for a class of nonlinear systems with unknown dynamics

Abstract

A neural-network-based finite-horizon optimal tracking control scheme for a class of unknown nonlinear discrete-time systems is developed. First, the tracking control problem is converted into designing a regulator for the tracking error dynamics under the framework of finite-horizon optimal control theory. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming algorithm is introduced to obtain the finite-horizon optimal controller to make the cost function close to its optimal value within an ε-error bound. Furthermore, in order to implement the algorithm via dual heuristic dynamic programming technique, three neural networks are employed to approximate the error dynamics, the cost function, and the control law, respectively. In addition, a numerical example is given to demonstrate the validity of the present approach.