Globally Stable Adaptive Tracking Control of a Wheeled Mobile Robot Using RBF Neural Network as Feedforward Compensator
In this paper,a new adaptive position tracking control strategy is proposed for a class of wheeled mobile robot systems where radial basis function (RBF) neural network (NN) is used to model the uncertainty.The so-called feedforward compensation scheme is developed where only the information of the reference position is employed as the NN input.The main advantage is that the global stability of closed-loop systems can be guaranteed and the NN approximation domain can be determined based on the reference signal a prior,which is different from the conventional adaptive neural network control (ANNC) schemes where only the semi-globally stable result can be obtained and no method is provided to determined NN approximation domain.Finally,a simulation is given to verify the effectiveness of the proposed control scheme.
Wheeled Mobile Robot Adaptive Neural Network Control (ANNC) Global Stability Determination of Approximation Domain
Jian Wu Dong Zhao Weisheng Chen
Department of Mathematics, Xidian University, Xian, 710071, China Department of Mathematics, Xidian University, Xi’an, 710071, China
国际会议
the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)
贵阳
英文
1888-1893
2013-05-01（万方平台首次上网日期，不代表论文的发表时间）