基于线性自抗扰的AGV轨迹跟踪控制器设计及参数优化

LADRC-based AGV trajectory tracking controller design and parameter tuning

针对自动导引车(AGV)的轨迹跟踪问题,通过将多入多出的非线性运动学系统解耦为多个单入单出的线性系统,在此基础上设计了基于线性自抗扰控制(LADRC)的轨迹跟踪控制器;引入混沌搜索对量子粒子群算法(QPSO)进行改进以解决其易于陷入局部最优的问题,然后利用混沌量子粒子群算法(CQPSO)对LADRC控制器的参数进行整定;最后分别就参考轨迹为直线和参考轨迹突变情况下的轨迹跟踪情况进行了仿真实验,实验结果证明了本文设计的轨迹跟踪控制器的有效性。

Automatic guided vehicles（ AGV） are an important transport tool in logistics systems. The AGV trajectory tracking problem is discussed in this work. The nonlinear multi-input multi-output control system is decoupled into several independent single-input single-output linear control loops by decomposing the velocity of AGV into the components in the X and Y directions. In addition,a controller for trajectory tracking of AGV is designed based on the linear active disturbance rejection control（ LADRC）. The paper introduces chaos search into the quantum-behaved particle swarm optimization（ QPSO） algorithm to solve the flaws of easy plunging into local optimum,and then the improved chaos quantum-behaved particle swarm optimization（ CQPSO） algorithm is used to tune the parameters of LADRC. The simulation results show that the proposed method is effective and feasible.