Learning from adaptive neural network output feedback control of a unicycle-type mobile robot. (March 2016) - Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying
Abstract: 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. Abstract : Highlights: We present a new method for output feedback control of unicycle-type mobile robots. The unknown system dynamics can be learned by using RBF neural networks. High-gain observers are designed to estimate the unmeasurable system states. Stability of the robot system and convergence of tracking errors are guaranteed. The learned knowledge of system dynamics can be recalled for similar control tasks.