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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。
The submarine is a multivariable, highly nonlinear system. For this special control object, S-plane control is a simple and practical control method. However, because of its lack of self-learning ability, the controller parameters need to be manually adjusted. Particle swarm optimization (PSO) algorithm can be used to deal with the problem of parameter tuning of S-plane controller. However, there are still some shortcomings of PSO algorithm such as premature convergence, easy falling into local extremum. In order to solve this problem, the SA algorithm is introduced to optimize the PSO algorithm, and the SA-PSO algorithm is proposed. This method not only realizes the self-adjusting of S-plane controller parameters, but also improves the S-plane control Optimization of the tuning parameters of the device. Finally, through the submarine’s motion control simulation test, the feasibility and superiority of this method are verified.