Fault-tolerant model predictive sliding mode control for trajectory replanning of multi-UAV formation flight

Abstract

To tackle the trajectory-following problem of multiple unmanned aerial vehicles (UAVs) characterized by high non-linearity and strong coupling, this paper methodologically separates the dynamics of fixed-wing UAVs into two subsystems and designs appropriate controllers for each loop. Unlike previous works, the proposed multi-purpose method simultaneously accounts for constraints, computational time, external disturbances, and actuator faults. The inclusive structure of the proposed strategy is as follows: Firstly, in the outer loop, by employing the high precision and constraint-handling attributes of nonlinear model predictive control (NMPC), the trajectories of the agents are guided to their reference positions while considering spatial limitations, including no-fly zone evasion and inter-vehicle collision evasion. Then, the optimal states of the inner loop are designed. Secondly, in the inner loop, a fault-tolerant sliding mode predictive control (SMPC) is reconfigured to accommodate identified actuator faults and follow the optimal states produced by NMPC. The effectiveness of the suggested algorithm is verified through a series of simulation results. Comparison simulation results substantiate the ascendancy of the suggested dual-loop method over the NMPC trajectory replanning algorithm.