Fixed‐time self‐structuring neural network cooperative tracking control of multi‐robot systems with actuator faults

Abstract

Abstract In this study, a fixed‐time adaptive cooperative controller by a self‐structuring neural network is proposed, and actuator faults are considered for multi‐robot systems. First, a novel fixed‐time leader state observer is developed to estimate the state information of the leader and pass on to other followers without measuring the leader's velocity. Second, a fixed‐time cooperative controller is designed to achieve fast response and high precision. Third, a fixed‐time convergent self‐structuring neural network is designed to improve the approximation accuracy affected by system uncertainties and actuator faults. A new neuronal splitting strategy is designed to avoid excessive computational burden caused by too many neurons. Next, the Lyapunov stability theorem is employed to demonstrate that the whole error closed‐loop system can globally converge to a small region around zero in a fixed time. Finally, a simulation example on multi‐robot systems shows that the proposed fixed‐time adaptive cooperative controller is able to obtain satisfactory performances in the presence of uncertainties from external disturbances, actuator faults and other causes.