Decentralized finite-time adaptive neural FTC with unknown powers and input constraints

This paper investigates the finite-time tracking problem of a class of nonlinear interconnected systems, where each subsystem not only is subject to actuator faults but also has unknown system input powers. Based on Lyapunov stability theory and decentralized control method, a novel finite-time adaptive fault-tolerant control (FTC) scheme is proposed such that each tracking error can converge to a small neighborhood of the origin in finite time. Different from the existing works on interconnected systems where the system input powers are assumed to be known and equal to one, the general case where the powers are unknown and larger than one is investigated in this work. Furthermore, input constraints are also applied to avoid actuator damage caused by control signal overshoot and jittering in this work. From theoretical analysis, each tracking error is proven to converge to a small neighborhood of the origin in finite time. Finally, a numerical example is given to demonstrate the validity of proposed control method.