摘要
针对带有执行器饱和的柔性关节机器人系统,提出一种位置反馈动态面控制,以实现机器人连杆的角位置跟踪.在一般动态面控制的设计框架下,设计观测器重构系统未知速度状态,利用径向基函数神经网络学习饱和非线性特性,结合"最小参数学习"算法减轻计算负担.通过Lyapunov方法证明得出闭环系统所有信号半全局一致有界,跟踪误差可以通过调节控制器参数达到任意小.仿真结果表明,控制系统能够克服外界干扰,有效补偿系统存在的执行器饱和,实现柔性关节机器人的准确跟踪控制.该方法避免了传统反演设计存在的"微分爆炸"现象,简化了设计过程.
The research explored the compensation of flexible-joint robot＇s actuator saturation using a dynamic sur- face controller for tracking control of link position. Under the design of a general dynamic surface control, an observer was designed to aid in the estimation of unknown velocity states. Radical basis function （RBF） neural net-work was used to examine saturation nonlinearity and ＂minimal learning parameter＂ technique for the reduction of computational burden. Based on the Lyapunov stability analysis, it was shown that the control strategy could guar- antee the semi-global stability of the closed-loop system and arbitrarily small tracking error by adjusting the control- ler parameters. The simulation results indicated that the proposed control system may overcome the external disturb- ances, compensate for the existing actuator saturation of systems effectively, and realize more accurate tracking control for flexible-joint robots. The proposal eliminates the problem of ＂explosion of complexity＂ existing in traditional backstepping approaches and simplifies controller design procedures plainly.
出处
《智能系统学报》
CSCD
北大核心
2013年第1期21-27,共7页
CAAI Transactions on Intelligent Systems
基金
教育部高等学校博士学科点专项科研基金资助项目(20121102110008)
关键词
柔性关节机器人
动态面控制
执行器饱和
神经网络
观测器
flexible-joint robots
dynamic surface control
actuator saturation
neural network
observer
