【摘要】 针对复杂环境中自治水下机器人(autonomous underwater vehicle,AUV)编队的避障控制问题，提出一种基于事件触发的模型预测控制(model predictive control,MPC)算法。建立水下机器人运动模型，结合领航-跟随式队形控制方法，利用领航AUV的位置信息和编队期望队形得到虚拟AUV的航行轨迹及速度信息，将其作为跟随AUV的航行参考轨迹；对传统人工势场法(artificial potential field,APF)进行适应性改进，以满足AUV编队在障碍物环境中避障规划的需求；设计一种基于跟随AUV轨迹预测值与实际值误差的事件触发机制来减少求解优化问题的计算量，降低计算负担。结果表明：与其他算法相比，该算法仿真结果具有可行性和有效性。更多还原

【Abstract】 An event-triggered model predictive control(MPC) algorithm is proposed to solve the obstacle avoidance problem of autonomous underwater vehicle(AUV) formation in complex environment. Tablishing a motion model of the AUV, combining a leader-follower formation control method, and obtaining the navigation track and speed information of a virtual AUV by using the position information of the leader AUV and an expected formation of the formation, and taking the navigation track and the speed information as the navigation reference track of a following AUV; The traditional artificial potential field(APF) method is improved adaptively to meet the needs of AUV formation obstacle avoidance planning in obstacle environment; An event-triggered mechanism based on the error between the predicted and actual values of the following AUV trajectory is designed to reduce the amount of calculation for solving the optimization problem and reduce the computational burden. The simulation results show that the proposed algorithm is feasible and effective compared with other algorithms.更多还原