基于粒子群算法优化的PID控制器的跨界机器人设计与控制

Y. Zennir, Sami Grief, El-Arkam Mechhoud
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引用次数: 1

摘要

本文介绍了一种采用元遗传算法、遗传算法和粒子群算法对PID控制器进行调整的跨座式四轮移动机器人的设计与控制。仿真采用的方法是一种非模态方法,因为它假设不知道系统的数学模型,实际上,机械结构是在SolidWorks下实现的,然后使用粒子群优化(PSO)技术进行仿真(SolidWorks, Simulink)控制器参数优化(PID)来控制每个车轮的转向角和角速度。实验结果清楚地说明了所选择的控制体系结构的有效性,并且采用粒子群算法的控制精度更高。在未来的工作中,我们将结果与其他优化算法(如GA(遗传算法)和GWO(灰狼优化器)算法进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Straddle Robot Design and control with a PID controller optimized by PSO algorithms
The work presented in this paper illustrates the design and control of a straddle robot-type four-wheel moving robot with PID controller adjusted by meta-genetic algorithms genetic Algorithm (GA) and PSO. The approach used for the simulation is a modeless approach because it assumes no knowledge of the mathematical model of the system, indeed, the mechanical structure was implemented under SolidWorks, then a simulation (Solidworks, Simulink) has was conducted using particle swarm optimization (PSO) techniques for controller parameter optimization (PID) to control the steering angle and angular velocity of each wheel. The results obtained clearly illustrate the effectiveness of the selected control architecture and the accuracy is better with the use of the PSO algorithm. In a future work, we compare the results with using other optimization algorithms like GA (Genetic Algorithm) and GWO (Grey Wolf Optimizer) algorithm.
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