基于HCOPSO算法的USV舵向PID控制参数整定方法

Parameter Tuning Method for USV Rudder Steering PID ControlBased on HCOPSO Algorithm

高速无人艇(USV)舵向控制要求同时满足调节时间短、超调量小,针对USV舵向比例积分微分(PID)控制的参数整定需求,将混合均值中心反向学习粒子群优化(HCOPSO)算法与PID控制结合,提出一种基于HCOPSO算法的USV舵向PID控制器参数整定方法。利用HCOPSO对PID控制器参数进行寻优,有效解决寻优过程的局部最优解问题。对比研究了粒子群(PSO)算法、线性惯性权重递减粒子群(LDIWPSO)算法、HCOPSO算法的PID控制器参数整定效果,结果表明, HCOPSO算法参数整定的USV舵向PID控制器具有更好的控制效果,相比于PSO、LDIWPSO,调节时间分别缩短22%、15%,超调量分别降低89%、74%,迭代次数分别减少40%、30%。基于研制的“久航750”USV开展了海洋环境测试,测试结果表明了文中设计方法应用于小型高速USV舵向控制的有效性。

The rudder steering control of high-speed unmanned surface vessels(USVs)must simultaneously satisfy therequirements of a short adjustment time and small overshoot.To satisfy the parameter tuning requirements for rudder steeringproportional integral derivative(PID)control of USVs,a parameter tuning method based on the hybrid mean center opposition-based learning particle swarm optimization(HCOPSO)algorithm was devised in this study.The HCOPSO algorithm was usedto optimize the parameters of the PID controller,and this prevented the optimization process from becoming trapped in localoptimal solutions.The PID controller parameter tuning effects of the particle swarm optimization(PSO),linear decreasinginertia weight particle swarm optimization(LDIWPSO),and HCOPSO algorithms were compared and studied.The resultsindicate that the USV rudder PID controller with the HCOPSO algorithm has the best control effect.Compared with those ofPSO and LDIWPSO,the adjustment time is reduced by 22%and 15%,the overshoot is reduced by 89%and 74%,and the number of iterations is reduced by 40%and 30%,respectively.Using the developed Jiuhang 750 USV,a marine environmenttest was performed.The test results indicate that the proposed method is effective for the rudder steering control of small high-speed USVs.