title: Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.
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.
authors.affiliation: School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China. Electronic address: zw0597@126.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Zeng
authors.affiliation: School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China.
authors.forename: Qinghui
authors.initials: Q
authors.lastname: Wang
authors.affiliation: School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China.
authors.forename: Fenglin
authors.initials: F
authors.lastname: Liu
authors.affiliation: School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
keywords: Adaptive output feedback; Deterministic learning; High-gain observer; Learning control; RBF neural network; Unicycle-type mobile robots