中文摘要：

潜器是一个多变量、高度非线性系统，对于这种特殊的控制对象，S面控制是一种简单实用的控制方法，但由于其不具备自学习能力，所以控制器参数需要人工调整。粒子群（PSO）算法可以用来处理S面控制器参数整定的问题，但PSO算法目前还存在着早熟收敛、易陷入局部极值等不足。针对此问题，引入模拟退火（SA）算法对PSO算法进行优化，提出模拟退火粒子群优化（SA-PSO）算法，此方法不仅实现了s面控制器参数的自调整，还提高了S面控制器参数整定的优化能力。最后，通过潜器的运动控制仿真试验，证实了该方法的可行性和优越性

英文摘要：

Underwater Vehicle is a multi-variable, highly nonlinear systems. For this particular control object , S Plane Control is asimple and practical control methods. Because its lack of self-learning capability, the parameters must be manually set for its controller. Particle Swarm （PSO） algorithm can be used to deal with the problem of S Plane Controller parameter tuning, but there is still lack of premature convergence and easy to fall into local minimum for it. Aiming at this problem, this paper introduces the simulated annealing （SA） algorithm to optimize the PSO algorithm and propose simulated annealing particle swarm optimization （SA-PSO） algorithm. This method not only to achieve the S plane of self-adjustment of controller parameters, but also improve the S plane controller＇ s parameter tuning optimization. Finally, the simulation experiments for underwater vehicle＇s motion control confirmed the feasibility and advantages.