Adaptive neural self-triggered bipartite consensus control for nonlinear fractional-order multi-agent systems with actuator fault

In this paper, the bipartite consensus tracking control problem is investigated for a class of nonlinear fractional-order multi-agent systems (FOMASs) with unknown dynamics, actuator faults, and input nonlinearities. Based on the adaptive backstepping technique, an adaptive bipartite consensus tracking control framework is constructed for FOMASs, where both cooperative and competitive relationships among agents are implemented. Furthermore, a fault compensation mechanism is proposed to relax the restriction on the number of actuators that can fail, and allow the existence of different types of input nonlinearities for each actuator. In addition, an improved adaptive self-triggered control mechanism that can be dynamically adjusted depending on the bipartite consensus error is extended to FOMASs to save network resources. Then, by means of the fractional-order Lyapunov stability criterion, it is theoretically proved that the proposed control scheme ensures that all signals of the closed-loop systems are bounded and drives the bipartite consensus error into a desired neighborhood of the origin. Finally, simulation results are provided to confirm the effectiveness of the proposed control scheme.