Finite-Horizon $H_{\infty }$ Tracking Control for Unknown Nonlinear Systems With Saturating Actuators.

In this paper, a neural network (NN)-based online model-free integral reinforcement learning algorithm is developed to solve the finite-horizon optimal tracking control problem for completely unknown nonlinear continuous-time systems with disturbance and saturating actuators (constrained control input). An augmented system is constructed with the tracking error system and the command generator system. A time-varying Hamilton-Jacobi-Isaacs (HJI) equation is formulated for the augmented problem, which is extremely difficult or impossible to solve due to its time-dependent property and nonlinearity. Then, an actor-critic-disturbance NN structure-based scheme is proposed to learn the time-varying solution to the HJI equation in real time without using the knowledge of system dynamics. Since the solution to the HJI equation is time-dependent, the form of NNs representation with constant weights and time-dependent activation functions is considered. Furthermore, an extra error is incorporated in order to satisfy the terminal constraints in the weight update law. Convergence and stability proofs are given based on the Lyapunov theory for nonautonomous systems. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.