摘要
粒子群优化算法(PSO)基于群体的演化算法,本质上是一种随机搜索算法,并能以较大概率收敛到全局最优。针对非线性机械臂系统,利用径向基函数(RBF)神经网络和PID控制器作为混合控制器,运用PSO算法对神经网络参数进行在线学习优化,同时在PID控制器的辅助下对机械臂系统进行在线自校正控制。计算机仿真表明,该控制器具有较高的控制精度和响应速度,可以满足机械臂工作要求。
Particle swam optimization （PSO） is an optimization technique based on evolutionary computation. The algorithm is a random searching algorithm in nature. It can couverge to the global minima more probability. Aim to a nonlinear robotic arm system, a radial basis function （RBF） neural network and PID control strategy were proposed. PSO was used for optimizing parameters of neural network controller, self-tuning control for the robot arm system was carried out by the PID controller. The simulation results show that the method is more precision and effective, it can content with the demand of robotic arm control.
出处
《机电工程》
CAS
2008年第1期44-47,共4页
Journal of Mechanical & Electrical Engineering
关键词
粒子群算法
径向基函数
神经网络
自校正控制
particle swam optimization （PSO）
radial basis function （RBF）
neural network
self-tuning control
