Particle swarm optimization (PSO) is a widely used method that can provide good parameters for the motion controller of mobile robots. In this paper, an improved PSO algorithm that optimize the control PID parameters of a specific robot have been proposed. This paper first presents a brief review of recently proposed PSO methods, and then presents a detailed analysis of the PID optimization algorithm, which uses H∞ theory to reduce the search space and fuses the information entropy to ensure the diversity of particles. Simulations in Matlab show that the algorithm can improve the convergence speed and get a better global optimization ability than the standard PSO algorithm. Experimental results present a sound effects for the control of the negative pressure adsorption motor in the power grid pipeline robot during its adsorption along the circular movements, which verifies the effectiveness of the proposed method.

粒子群优化（PSO）是一种可以为移动机器人的运动控制器提供良好参数的广泛使用的方法。在本文中，提出了一种改进的PSO算法，该算法优化了特定机器人的控制PID参数。本文首先简要介绍了最近提出的PSO方法，然后对PID优化算法进行了详细分析，该算法使用H∞理论来减少搜索空间并融合信息熵以确保粒子的多样性。在Matlab中的仿真表明，与标准PSO算法相比，该算法可以提高收敛速度，并具有更好的全局优化能力。