ADP-Based Finite-Horizon Control for Discrete-Time Systems with Multi-Inputs and Actuator Saturations

In this article, a neural network (NN) methodology is raised to resolve the optimum control issue for affine nonlinear systems with multi-input constraints over a finite-horizon. Unlike the value and policy iterations in conventional approximation dynamic programming (ADP) technology, which usually require a sufficient amount of iterations and multiple iteration loops to ensure the steadiness of the system and the convergence of system states and control strategies, a finite-horizon constrainedinputs optimum control methodology relying on the actor-critic structure is presented. This method can be utilized as time goes forward. The final cost function is considered, although the system state values converge to zero in the finite-horizon issue. The stability of the presented control algorithm is examined by means of the Lyapunov stability principle. Ultimately, a simulation case is provided to demonstrate the feasibility and effectiveness of the devised algorithm.