Fault-Tolerant Trajectory Tracking Based on Improved Chaotic Firefly Algorithm Model Prediction Control for Human-Occupied Vehicle With Ocean Current
针对载人潜水器在海洋环境中模型不确定和低速问题，提出一种级联控制器，利用改进混沌萤火虫算法与模型预测控制结合，解决推进器故障导致的速度跳变和驱动饱和，实现稳定轨迹跟踪。
In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem.