Model Reference Adaptive Control Based on Neural Network
In this paper, an approach to model reference adaptive control based on neural networks is pro- posed for a class of nonlinear dynamical systems. The controller structure can employ a radial basis function network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using σ-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neigh-borhood of zero, whose size is evaluated and depends on the approximation error of the neural network. Simulation results showing the feasibility and performance of the proposed approach are given.
Huiming Liu Qishen Gao
School of Information Science and Engineering Northeastern University Shenyang, China 110004 Institute of Complexity Science Qingdao University Qingdao, China 266071
国际会议
青岛
英文
2006-07-21（万方平台首次上网日期，不代表论文的发表时间）