多机械臂的分布式自适应迭代学习控制

针对拓扑结构为无向连通的多机械臂系统,提出了一种自适应与迭代学习相结合的分布式控制协议来实现整个系统对给定期望参考轨迹的一致性跟踪.通过引入一个适当的自适应迭代学习参数,所提自适应迭代学习控制协议能够克服机械臂系统中的干扰和模型不确定性,并且每个机械臂的自适应迭代学习控制(AILC)律仅需要利用其与邻居机械臂的相对交互信息.进一步,在只有一部分机械臂具有期望参考轨迹信息的前提下,该控制协议可以实现整个系统对期望参考轨迹的跟踪,同时能够保证轨迹跟踪误差与控制输入的有界性.此外,利用李亚普诺夫分析方法证实了所得结论的正确性,并且通过一个实例验证了所提自适应迭代学习控制协议的有效性. 

Distributed adaptive iterative learning control for multiple robot manipulators

A hybrid adaptive and iterative learning method was proposed to obtain distributed control protocols for multiple manipulator systems with undirected interaction topology to achieve consensus tracking of the specified desired reference trajectory. By introducing an appropriate adaptive iterative learning parameter, the proposed adaptive iterative learning control (AILC) protocol can overcome the effects of disturbances and model uncertainties of manipulators, where the AILC law of each manipulator needs only the relative information between it and its nearest neighbors. Moreover, it is shown that all manipulators can be rendered to achieve the perfect tracking of the desired reference trajectory though its information can be accessed by only a portion of manipulators, where the boundedness of both the tracking error and the control input can be simultaneously guaranteed. In addition, the Lyapunov analysis method is employed to validate the obtained results, and the effectiveness of the proposed AILC protocol is illustrated through an example.