一类非仿射输入和不确定性系统的输出跟踪控制

针对一类具有非仿射输入和不确定性的混沌系统,提出了鲁棒自适应RBFNN反演控制。RBF神经网络用来逼近设计过程中所有的未知非线性函数,并且对逼近的误差设计了鲁棒项,设计的自适应律是针对RBF所有权值的上界而不是权值本身,因而在线自适应参数数量减少了,节约了计算时间。该方法保证了混沌系统的输出能跟踪任意参考信号,并且跟踪误差是一致渐进收敛的。仿真结果表明该方法的可行性和有效性。

Output Tracking Control of System with Uncertainty and Non-Affine Inputs

A robust adaptive radial basis function neural network （RBFNN） control scheme based on back stepping approach was presented for a class of systems with non-affine input, modeling uncertainties and external disturbances. RBFNNs were employed to approximate the unknown parts of the virtual control and practical controls. The robust term was designed to compensate for approximation error. The adaptive laws were designed for the bound of parameters of RBFNN, so the number of online adaptive parameters was reduced. The output of system could track arbitrary reference signal and the tracking errors were guaranteed to uniformly asymptotically converge to zero. Simulation result illustrated the feasibility and effectiveness of the proposed control technique.