Zeng Wei, Wang Qinghui, Liu Fenglin, Wang Ying
School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China.
School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China.
ISA Trans. 2016 Mar;61:337-347. doi: 10.1016/j.isatra.2016.01.005. Epub 2016 Jan 29.
This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.
本文研究非完整单轮式移动机器人的自适应神经网络（NN）输出反馈控制学习。主要困难源于未知的机器人系统动力学和不可测量的状态。为克服这些困难，提出了一种新的自适应控制方案，包括设计一种新的自适应NN输出反馈控制器和两个高增益观测器。结果表明，闭环机器人系统的稳定性和跟踪误差的收敛性得到了保证。未知的机器人系统动力学可以用径向基函数神经网络来近似。当重复相同或相似的控制任务时，可以调用和重用所学知识，以实现有保证的稳定性和更好的控制性能，从而避免神经网络的大量重复训练过程。
