基于改进灰狼算法的自动导航小车控制策略

针对灰狼算法(grey wolf optimizer, GWO)易陷入局部最优、后期收敛速度慢等问题，通过引入改进Tent混沌映射反向学习策略和非线性收敛因子，并加入差分进化的变异、交叉、选择操作，提出一种改进的差分灰狼优化算法(improved differential evolution grey wolf optimizer, IDE-GWO)。将改进算法应用于优化自动导航小车(automated guided vehicle, AGV)的比例积分微分(proportion integration differentiation, PID)控制参数，并与其他几种算法进行对比。Simulink仿真实验结果表明：该改进算法优化PID参数的控制效果明显优于其他智能优化算法，能够有效地提升AGV轨迹跟踪性能，使得AGV实际轨迹能较好拟合目标轨迹。

AGV Control Strategy Based on Improved Gray Wolf Algorithm

Aiming at the problems that the Grey wolf optimizer (GWO) is easy to fall into the local optimum and the late convergence speed is slow, an improved Tent chaotic map reverse learning strategy and nonlinear convergence factor are introduced, and the variation, crossover and selection of differential evolution are added. Operation, an improved differential evolution grey wolf optimizer (IDE-GWO) is proposed. The improved algorithm is applied to optimize the PID control parameters of the AGV car, and compared with ABC, DE, PSO, GWO and WOA, the Simulink simulation experiment results show that the control effect of the improved algorithm to optimize the PID parameters is significantly better than other intelligent optimization algorithms. It can effectively improve the AGV trajectory tracking performance, so that the actual AGV trajectory can better fit the target trajectory.