粒子群神经网络辨识的机器人分数阶滑模控制

Fractional order sliding mode control of industrial robot based on particle swarm optimization neural network

研究了一种粒子群算法优化的神经网络分数阶滑模变结构控制方法,并将其应用到工业机器人路径跟踪研究中。首先采用粒子群算法优化的神经网络辨识工业机器人的系统模型,训练得到与系统控制参数解析度最相关的模型;然后基于分数阶理论与滑模变结构理论设计了分数阶滑模变结构控制器,作为系统的主控制器应用到工业机器人轨迹跟踪控制系统中。仿真及实验结果表明,该方法具有良好的跟踪性能和快速性。

A method of fractional sliding mode variable structure control method based on neural network optimized by particle swarm optimization(PSO)is studied and applied to the path tracking of industrial robots.Firstly,the neural network optimized by particle swarm optimization is used to identify the system model of industrial robots,and the model most relevant to the resolution of system control parameters is trained.Then,the fractional order sliding mode variable structure controller is designed based on fractional order theory and sliding mode variable structure theory,which is applied to the path tracking of industrial robots as the main controller.Simulation and experimental results show that this method has good tracking performance,fast and robust.