基于改进的PSO在并联机构神经网络控制系统中的应用（英文）  

Application of improved PSO-based to neural network control system of parallel mechanism

作　　者：王长建[1] 王鹏[1] 

机构地区：[1]长江大学机械工程学院,湖北荆州434000

出　　处：《机床与液压》2015年第12期60-64,70,共6页Machine Tool & Hydraulics

摘　　要：针对传统PID神经网络不能实时有效地控制非线性多变量系统的问题,设计了一种新型多变量自适应PID神经网络控制器。该控制器的隐含层带有输出反馈和激活反馈,实现了比例、微分和积分功能。利用一种基于解空间划分的改进粒子群算法对控制器参数进行优化,消除了初始值对控制器准确性的影响,并将控制器应用于并联机构控制中。由仿真结果可知:控制器控制精度高,鲁棒性和自适应性较强。这一研究为并联机构的精准控制和优化设计提供了理论基础。As the traditional PID neural network could not effectively control the real-time nonlinear muhivariable system, this paper proposed a new type of multivariable adaptive PID neural network controller. This control sys- tem could put out feedback and activation feedback, with the function of proportion, integration and differentiation. We used the Particle Swarm Algorithm which is based on the solution space division to optimize the parameters of the controller. It also could eliminate effect of initial values on the accuracy of the controller and can be applied to the parallel mechanism control system. As the simulation results shown, controller had higher preci- sion, better robustness and adaptability. This research provided a theoretical basis for the optimization design and performance analysis of the parallel mechanism.

关 键 词：PID神经网络 并联机构 改进PSO算法 

分 类 号：TH165.2[机械工程—机械制造及自动化]