摘要
针对系统参数完全未知且仅输出可测的机器人,使用径向基函数(RBF)神经网络和高增益观测器设计了一种自适应神经控制算法.该算法不仅实现了闭环系统所有信号的最终一致有界,而且沿周期跟踪轨迹实现了对未知闭环系统动态的确定学习.学过的知识可用来改进系统的控制性能,也可应用于后续相同或相似的控制任务以节约时间和能量.仿真研究表明了所设计的控制算法的正确性和有效性.
An adaptive neural control algorithm is proposed for completely unknown robot with only output measurement using RBF networks and high-gain observer.The designed adaptive neural controller not only guarantees uniformly ultimately bounded of all signals in the closed-loop system,but also achieves the deterministic learning of the unknown closed-loop system dynamics along periodic tracking orbit.The learned knowledge can be used to improve control performance,and can also be recalled and reused in the same or similar control task to save time and energy.Simulation results show the effectiveness of the proposed approach.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第11期1740-1744,1750,共6页
Control and Decision
基金
国家自然科学基金项目(60743011
61075082)
