基于预测和粒子群的船舶避碰与路径跟踪控制

Path following and collision avoidance of underactuated ships based on model predictive control and modified particle swarm optimization

针对欠驱动水面船舶的路径跟踪及静态和动态障碍物避碰问题,提出一种综合模型预测控制和改进粒子群优化(Modified Particle Swarm Optimization, MPSO)算法的控制策略。采用视线制导律(Line-of-Sight, LOS)与积分视线制导律(Integral Line-of-Sight, ILOS)相结合的方式解决船舶欠驱动问题并补偿漂角引起的稳态横向偏差;以减小船舶实际艏向角与期望艏向角的偏差及舵角变化率作为非线性优化目标,使用非线性模型预测控制(Nonlinear Model Predictive Control, NMPC)方法实现航向控制,并将避碰因数作为一种时变非线性惩罚项加入目标函数中;为求解非线性约束优化问题,采用MPSO算法进行求解以获得控制指令。以ONRT(Office of Naval Research Tumblehome)船模为研究对象进行静态和动态障碍物避碰情况下的仿真试验,并与线性粒子群优化(Linear Particle Swarm Optimization, LPSO)算法的结果进行比较,验证所提出方法的有效性。

A control method combining the MPC(Model Predictive Control)and MPSO(Modified Particle Swarm Optimization)is developed for path following of underactuated surface vessels and avoidance of static or dynamic obstacles.The combination of LOS(Line-Of-Sight)guidance and ILOS(Integral Line-Of-Sight)guidance is used to solve the problem of underactuated system and to compensate the steady state cross-track error caused by drift.A nonlinear optimization problem is formulated to minimize the deviation of actual heading from desired heading and the rate of change of rudder angle.NMPC(Nonlinear Mode Predictive Control)method is used to realize heading control,meanwhile,the collision avoidance factors are embedded in the path following control problem as a nonlinear penalty term.The MPSO algorithm is used to solve the nonlinear optimization problem and generate control commands.Simulation experiments under the condition of static and dynamic obstacles are carried out with the ONRT(Office of Naval Research Tumblehome)model,and the results are compared to those from LPSO(Linear Particle Swarm Optimization)algorithm to verify the effectiveness of the proposed method.