摘要
针对一类非线性不确定连续系统,提出了一种基于径向基(RBF)神经网络的模型参考自适应控制方案。控制器的非线性部分由RBF神经网络实现,根据系统输出与参考模型输出之间的误差调整神经网络的权值,以补偿系统中的非线性因素。引入权值学习误差的概念,以此为基础利用李雅普诺夫原理分析推导了网络权值的调整规律,并证明了系统的稳定性。在单级火箭速度控制中应用该方案进行了设计,仿真结果表明,火箭速度3s后即能完全跟踪参考模型的输出;RBF神经网络在2s后即能逼近非线性项,网络权值收敛。
An approach to model reference adaptive control based on neural network is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure employs 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 aσmodification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. The adaptive control scheme is utilized in a rocket velocity control system. Simulation results showing the feasibility and satisfactory performance of the proposed approach are given.
出处
《弹箭与制导学报》
CSCD
北大核心
2006年第SA期1168-1171,共4页
Journal of Projectiles,Rockets,Missiles and Guidance
