水下机器人S面控制器的改进粒子群优化

S surface controller for underwater vehicles using particle swarm optimization

S面控制器是一种有效的水下机器人的运动控制算法,但需要人工调整参数.为了减少手工调整参数所带来的困难和误差,提出了改进的粒子群优化算法对S面控制器参数进行优化.采用动态压缩因子,加快粒子算法的收敛;引入退火算法,提高粒子算法的局部搜索能力.同时,在S面控制器中引入智能积分项,有效地减小控制器的稳态误差.最后,论述了改进粒子群算法优化S面控制参数的具体过程,并在某型水下机器人上进行了仿真试验和水池试验.试验结果表明,该算法对于水下机器人非线性控制器的参数寻优达到良好的效果,优化后的S面控制具有较快的反应速度和较小的超调.

S surface control has been proven effective in motion control of underwater vehicles, but until now its parameters required human adjustment. In order to reduce the difficulty and error involved in manual parameter adjustment, a particle swarm optimization （PSO） method for S surface control was developed. A dynamic compressibility factor was introduced to accelerate the convergence of the PSO, and annealing was used to improve search ability. Moreover, an intelligent integral of S surface control was introduced to eliminate steady-state errors. Finally, details of the improved PSO method for S surface control are discussed and results are presented from experiments carried out on the ＂ XX＂ mini underwater vehicle. The results show that the improved PSO gets results closer to the global optimum and converges faster. The optimized parameters of S surface control greatly improve responsiveness and overshoot.