摘要
为了提高传统PID控制效率,解决传统PID控制不能实时调节参数的问题,文章提出了针对PID控制的算法改进。首先引入了小车模型,建立移动机器人的数学模型。之后进行算法改进,建立基于模糊控制的PID系统;引入粒子群算法,使用粒子群算法优化模糊控制PID算法。最后在Matlab中进行仿真实验,并在Simulink中搭建相应模块,检测移动机器人避障效果,分析实验避障性能。试验结果表明,使用模糊控制PID算法可以有效地提高控制效率,使用粒子群算法优化模糊控制PID算法的控制效果最优秀;两种控制方法都可以降低超调量,提升系统反应速率,有效地提升控制效率。
In order to improve the efficiency of traditional PID control and solve the problem of traditional PID control not being able to adjust parameters in real-time,algorithm improvements for PID control are proposed.Firstly,a small car model was introduced to establish a mathematical model of the mobile robot.Afterward,algorithm improvement will be carried out to establish a PID system based on fuzzy control.The particle swarm optimization algorithm will be introduced and used to opti-mize the fuzzy control PID algorithm.Finally,simulation experiments were conducted in Matlab,and corresponding modules were built in Simulink to test the obstacle avoidance effect of the mo-bile robot and analyze the experimental obstacle avoidance performance.The experimental results show that using the fuzzy control PID algorithm can effectively improve control efficiency,and using the particle swarm optimization algorithm to optimize the fuzzy control PID algorithm has the best control effect.Both control methods can reduce overshoot,improve system reaction rate,and effec-tively enhance control efficiency.
作者
庞淞友
于莲芝
Songyou Pang;Lianzhi Yu(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai)
出处
《建模与仿真》
2025年第5期223-232,共10页
Modeling and Simulation
关键词
机器人避障
PID控制
模糊控制
粒子群优化算法
Robot Obstacle Avoidance
PID Control
Fuzzy Control
Particle Swarm Optimization Algorithm
