Design of Excavator PD Controller Based on Particle Swarm Optimization Algorithm
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摘要: 针对挖掘机精确轨迹控制问题,构建了挖掘机动力学模型,搭建了Simulink仿真环境下挖掘机动力学模块、PD控制系统模块和可自动调用的PD参数输入模块;通过PSO最优搜寻得到PD权重参数,然后在PSO程序中定义挖掘机动力学模型,最后在动态控制下进行一个闭环快速自适应整定联合仿真。仿真结果表明,采用基于粒子群算法的挖掘机PD控制器与传统的试凑法都能达到期望的轨迹控制,且都能较好地贴近理论值,但基于粒子群算法的挖掘机PD控制器能够快速自适应整定。在挖掘轨迹起始过程中,相较于试凑法中KP和KV组合为200-5,粒子群算法的PD控制器要收敛近75%,降低了系统的稳态误差,大大提高了液压挖掘机的稳定性和精确性。Abstract: Aiming at the problem of precise trajectory control of excavator, the dynamics model for excavator is built, and the automatic call of excavator dynamics module, PD control system module and PD parameter input module is built under the Simulink simulation environment. The PD weight parameters are obtained through the PSO optimal search. The excavator dynamics model is defined in the PSO program, and a closed-loop fast adaptive tuning joint simulation is carried out under the dynamic control. The simulation results show that the excavator PD controller based on the particle swarm optimization algorithm can achieve the desired trajectory control compared with the traditional trial and error method, and both of them are close to the theoretical value; but the excavator PD controller based on the particle swarm optimization algorithm can quickly adapt to the adjustment, and it converges by 75% compared with the 200-5 combination of KP and KV in the trial and error method in the initial process of the excavation trajectory, which reduces the steady-state error of the system. It greatly improves the stability and accuracy of hydraulic excavator.
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Keywords:
- excavating machinery /
- dynamics /
- PD controller /
- particle swarm optimization
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