Fixed-Time and Prescribed-Time Fault-Tolerant Optimal Tracking Control for Heterogeneous Multiagent Systems

This study investigates the fixed-time and prescribed-time optimal formation control strategies for heterogeneous multiagent systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) unde actuator faults. In the proposed control framework, the critic-actor framework is designed to finish the optimization tracking. Radial basis function neural network (RBFNN) is implemented to derive the tracking control, in which the actor RBFNN is utilized to make up for the actuator faults and generate the formation control action, and the critic RBFNN is utilized to evaluate the execution cost. Then, a Lyapunov-based tracking technique is designed to ensure the fixed-time stability of the tracking error. Since the adaptive updating protocols are developed by deriving the gradient descent of the cost function, the optimized control algorithm can be obtained. In addition, a prescribed-time fault-tolerant optimal controller is further proposed, which renders the convergence time fully independent of any other parameter and the initial states, thus the convergence time can be uniformly prespecified. Finally, the validity of the proposed algorithms are demonstrated via the simulation experiments
Note to Practitioners
—Actuator faults often occur in the operation of industrial automation equipment. Hence, it is of great practical significance for control systems to have faster fault-tolerant performance. In addition, the optimization effect of performance indicator often denotes the quality of the expected task completion. Therefore, this study proposes a faster optimal fault-tolerant formation strategy for heterogeneous unmanned formation system based on fixed-time and prescribed-time theorems under actuator faults. The effectiveness of the developed control approach is verified by designed simulation.