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针对AGV轨迹跟踪问题,采用粒子群算法,设计了一种改进型粒子群算法的AGV控制器。将小车的距离偏差和角度偏差作为模糊变量的输入变量,而将小车相对应的能改变偏差的电压模拟量作为输出变量,分别对基于改进型粒子群算法的PID控制器采用Matlab软件仿真比较,并对过程收敛曲线平滑度进行分析仿真结果表明,基于改进型粒子群算法的PID控制器能够有效的解决大偏差情况下控制不理想的问题。
Aiming at AGV trajectory tracking problem, an improved particle swarm optimization AGV controller is designed by using particle swarm optimization algorithm. The distance deviation and angle deviation of the car are taken as the input variable of the fuzzy variable, and the voltage analogue which can change the deviation corresponding to the car is taken as the output variable. The PID controller based on the improved particle swarm optimization algorithm is compared and simulated by Matlab software, And the process of the convergence curve smoothness analysis results show that the improved particle swarm optimization PID controller can effectively solve the case of large deviation control is not ideal.