Homing tracking control of autonomous underwater vehicle based on adaptive integral event-triggered nonlinear model predictive control. (1st June 2023) - Wu, Wenhua; Zhang, Wei; Du, Xue; Li, Zixuan; Wang, Qiang
Abstract: The homing problem of autonomous underwater vehicle with constraints and bounded disturbances is studied in this paper. More specifically, a nonlinear model predictive control (NMPC) framework based on adaptive integral event-triggered (AIET) mechanism is designed for AUV tracking mobile docking station (DS). Firstly, according to the characteristics of AUV homing, a guidance system based on mobile DS is designed, which can provide a feasible reference trajectory for the subsequent AUV tracking independent of the vehicle model. Then, in the framework of AIET-NMPC control, the designed tracking controller can reduce the computational cost while guaranteeing the control accuracy. The proposed AIET mechanism can dynamically adjust the trigger threshold according to the integral error between the real state and the optimal state of the system. In addition, by designing exponential robust constraints in the NMPC optimization problem, the system stability margin is increased. Finally, it strictly proves the Zeno free behavior, the feasibility of the algorithm, and the stability of the system. Simulation results demonstrate that the proposed control approach yields better effects in terms of tracking control performance and computational performance compared to other methods. Highlights: A feasible guidance trajectory based on the mobile DS is generated through using geometrical analysis method, which provides a reference trajectory for the design of the tracking controller. A novel adaptive integral event-triggered nonlinear model predictive control (AIET-NMPC) is designed to reduce the computational burden. Aiming at the trajectory tracking NMPC optimization problem, a new exponential robust constraint of system state is proposed to improve the stability margin of the closed-loop system. The sufficient conditions to ensure the algorithm feasibility and system stability are strictly derived.