LAUSR

This paper addresses the adaptive finite-time tracking control problem for a class of multi-agent high-order systems with actuator faults. By adding a power integrator and taking advantage of the approximation capabilities of radial basis function in neural networks, an adaptive backstepping controller is developed to overcome difficulties associated with the positive odd integer terms of nonlinear multi-agent high-order systems. Moreover, fault-tolerant control is used to tackle the impact of actuator failures. The developed adaptive finite-time control scheme ensures that the outputs of all followers track synchronously the reference signal quickly in finite time, and all signals of the controlled system are semi-globally uniformly finite-time stable. Simulation results demonstrate the feasibility of the proposed scheme.