Adaptive prescribed performance consensus tracking for uncertain delayed multiagent systems via command filtered output feedback

This article investigates the adaptive fixed-time prescribed performance (FTPP) consensus tracking control problem for uncertain nonstrict-feedback multiagent systems with unmeasured states and time-varying delays. First, a piecewise function is proposed to characterise FTPP and eliminate the initial value limitations present in traditional prescribed performance control methods. To ensure that tracking errors satisfy prescribed performance, barrier functions are further constructed and introduced into the control design process. Second, based on the approximation of neural networks, adaptive neural state observers are designed to estimate the unmeasured states. Then, an adaptive FTPP consensus control scheme is developed based on command filtered backstepping technique and Lyapunov-Krasovskii functional. It guarantees that (1) all signals in the closed-loop system are semiglobally uniformly ultimately bounded; and (2) for any bounded initial values, all followers' outputs can track the leader's output within a prescribed fixed-time and tracking accuracy, while satisfying the required transient tracking performance. Finally, the effectiveness of the proposed control scheme is verified through simulation studies.