自治水下机器人（Autonomous Underwater Vehicle，AUV）轨迹跟踪控制是AUV运动控制的关键技术之一，其控制器设计的优劣影响最终的跟踪性能。本项目针对动态海流影响、模型不确定性与外界扰动、驱动饱和几个关键问题，系统研究AUV的三维轨迹跟踪控制问题。对于驱动饱和问题，提出以模型预测控制（model predictive control，MPC）为基础的轨迹跟踪控制算法。利用MPC的约束特性，满足AUV的驱动饱和约束；同时考虑利用量子粒子群算法优化控制律，降低跟踪误差。对于动态海流影响，利用海流观测器与速度合成解决海流环境下的跟踪问题。针对模型不确定性与外界扰动问题，构造模型预测与滑模控制（MPCSMC）级联控制系统，利用滑模控制的鲁棒特性，克服AUV在复杂水下环境下的模型不确定性与外界扰动影响。最后，进行系统仿真研究并在“海事号”AUV上进行水池实验验证。

Trajectory tracking control of Autonomous Underwater Vehicle (AUV) is one of the key technologies of AUV motion control. The pros and cons of its controller design have a great influence on the final tracking performance. This project is proposed in view of system research for AUV three-dimensional trajectory tracking control focusing on the key problems of dynamic current influence, model uncertainty, external disturbance and actuator saturation. For the actuator saturation problem, the trajectory tracking control algorithm is put forward on the basis of model predictive control. By constraint characteristics of MPC, the AUV actuator saturation constraint can be satisfied. At the same time, Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is considered for the optimization design of the control law to reduce the tracking error. For the effect of dynamic ocean current, the ocean current observer and velocity synthesis method is proposed to solve tracking problem under the ocean current environment. For the model uncertainty and external disturbance problem, the design of model predictive control and sliding mode control (MPC-SMC) cascade control system is proposed. By using the robust characteristics of sliding mode control, the effect of AUV model uncertainty and external disturbance in the complex underwater environment can be overcome. Finally, the system simulation research is conducted and pool experiments verified on the "Maritime" AUV.