论文部分内容阅读
针对一类多输入多输出(MIMO)仿射非线性动态系统,提出一种基于极限学习机(ELM)的鲁棒自适应神经控制方法.ELM随机确定单隐层前馈网络(SLFNs)的隐含层参数,仅需调整网络的输出权值,能以极快的学习速度获得良好的推广性.在所提出的控制方法中,利用ELM逼近系统的未知非线性项,针对ELM网络的权值、逼近误差及外界扰动的未知上界值分别设计参数自适应律,通过Lyapunov稳定性分析可以保证闭环系统所有信号半全局最终一致有界.仿真结果表明了该控制方法的有效性.
A robust adaptive neuron control method based on extreme learning machine (ELM) is proposed for a class of multiple input multiple output (MIMO) affine nonlinear dynamic systems. The ELM stochastically determines the hidden states of single hidden layer feedforward networks (SLFNs) In the proposed control method, the ELM is used to approximate the unknown nonlinearity of the system, and the weights of the ELM network , Approximation errors and unknown upper bounds of external disturbances are respectively designed parameter adaptive law. By Lyapunov stability analysis, all the signals in the closed-loop system can be guaranteed to be uniformly and semi-globally bounded. Simulation results show the effectiveness of the proposed control method.