摘要
为了解决具有状态约束的机械臂的控制问题,本文针对一类具有全状态约束和状态不完全可测的切换严格反馈非线性系统进行研究,通过引入状态观测器、自适应神经网络和动态表面控制技术,设计了一种基于径向基函数(RBF)神经网络的自适应输出反馈控制方法。利用Lyapunov方法和平均驻留时间理论(ADT)保证了闭环系统所有信号是半全局一致最终有界的(SGUUB),通过数值例子仿真验证了所提方法的有效性。最后将该方法应用于带电机驱动的机械臂并进行仿真实验,仿真结果表明,机械臂轨迹跟踪误差很小,有着良好的控制精度,同时也表明所提出的控制算法能够应用于实际工程模型。
In order to control manipulator switching with state constraints,this paper studies a class of strict-feedback nonlinear switching systems with full state constraints and incompletely measurable states.By introducing state observer,adaptive neural network and dynamic surface control technique,an adaptive output feedback control method based on the radial basis function(RBF)neural network is designed.The Lyapunov method and the average dwell time theory(ADT)are used to ensure that all signals in the closed-loop system are uniformly ultimately bounded.Numerical examples show the effectiveness of the semi-globally proposed method.Finally,the mechanical arm with motor drive is simulated,and the simulation results show that the trajectory tracking error of the manipulator is very small and has good control accuracy and that the proposed control method can be applied to actual engineering models.
作者
万敏
杨山山
WAN Min;YANG Shanshan(College of Mechanical and Electrical Engineering,Southwest Petroleum University,Chengdu 610000,China)
出处
《机械科学与技术》
CSCD
北大核心
2023年第4期597-607,共11页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51875489)。
关键词
动态面控制
全状态约束
非线性切换系统
神经网络状态观测器
机械臂轨迹控制
dynamic surface control
full state constrains
nonlinear switching system
neural network state observer
manipulator trajectory control
