Prescribed-Time Optimal Adaptive Tracking Control via Command Filters for Uncertain Multiagent Systems

Abstract

This article focuses on the issue of the prescribed-time optimal control for strict-feedback nonlinear multiagent systems with unknown time-varying parameters and unmodeled dynamics. Based on command-filtered backstepping, the controller is divided into two parts including feedforward and optimal feedback controllers. The former adopts dynamic surface control framework, in which a dynamic signal is used to dispose of unmodeled dynamics and a compensation signal is introduced on the basis of dynamic surface control to eliminate the filter errors. In addition, a novel prescribed-time scaling function and coordinate transformation are introduced. The latter adopts the adaptive dynamic programming methodology and an auxiliary dynamic system is generated to optimize the value function. Meanwhile, integral reinforcement learning technology is incorporated into adaptive dynamic programming structure to address the situation of unknown system's drift dynamics and time-varying parameters. The theoretical study leads to the following conclusion that the closed-loop system is semiglobally uniformly ultimately bounded, and the consensus errors can be restricted to a predefined region within a predefined time. In the interim, minimization is achieved in the cost functions. Lastly, the simulation results are given to show that the proposed adaptive control strategy is feasible.