基于高斯RBF神经网络的可伸缩机械臂系统动态建模与仿真

设计了一种新的多模块机器人机械臂系统,该机械臂的每个模块具有可伸缩杆和旋转节两部分.为了进行有效控制,给出了系统的解析公式和机械臂自适应控制的相关技术.使用高斯函数的RBF神经网络对系统的动态方程参数进行了估计,使用李亚普诺夫稳定性分析确定神经网络权重的自适应修正规则,并给出了相应的自适应控制器模型.对该机械臂系统的控制进行了计算机仿真分析,结果表明:该方法在响应时间和控制精度方面都有提高,并且在有负载等非线性事件突然干扰的情况下亦能进行有效控制.

Dynamic Modeling and Control of a Deployable Manipulator System Based on Gaussian RBF Neural-Network

An effective neural network based on adaptive controller for a newly designed multi-module manipulator system is presented in this paper.Each module of this multi-module manipulator system has a deployable link as well as a revolute joint.The analytical formulation of the system is developed for the purpose of effective control. And the relevant techniques of adaptive control of robot manipulators are derived.Then neural network with Gaussian radial basis functions is used to approximate the unknown terms in the dynamical equations of the manipulator.Lyapunov stability analysis is used to find an adaptive update rule for tuning the weight of the neural network.The corresponding adaptive controller is derived based on this approach.At last,the applicability of the control scheme for this manipulator system is tested through computer simulation