Neural-adaptive specified-time constrained consensus tracking control of high-order nonlinear multi-agent systems with unknown control directions and actuator faults

Abstract

In this paper, a specified-time neural-adaptive control algorithm is developed to solve the consensus tracking control problem of high-order nonlinear multi-agent systems with full-state constraints, unknown control directions, and actuator faults, which is intrinsically challenging due to the existence of high-order (positive odd integers greater than one) terms. More precisely, a novel specified-time performance function (STPF) is skillfully incorporated into the time-varying high-order log-type barrier Lyapunov function (BLF) to guarantee that the tracking errors remain under time-varying constraints within specified time. To handle the mixed unknown control directions, the hybrid Nussbaum function is innovatively employed in the distributed high-order nonlinear multi-agent scenario. By combining radial basis function neural networks (RBFNNs) with the adding-one-power-integrator technique, an adaptive approximation policy is introduced to derive the neural adaptive controllers. Moreover, actuator faults are also considered in this work. The variable-separable lemma is exploited to extract the fault signals in a “linear-like” manner. Comparative simulations are provided to demonstrate the effectiveness of the designed control framework.