基于ELM的一类MIMO仿射非线性系统的鲁棒自适应控制

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.

Robust adaptive control for a class of MIMO affine nonlinear systems using extreme learning machine

Based on the extreme learning machine(ELM), a robust adaptive neural control method for a class of multipleinput-multiple-output(MIMO) affine nonlinear dynamic systems is presented. ELM for single-hidden layer feedforward networks(SLFNs), which randomly chooses hidden node parameters and analytically determines the output weights of SLFNs, shows good generalized performance at extremely fast learning speed. The proposed control scheme utilizes the ELM to approximate the plant’s unknown nonlinear terms. Meanwhile, output weights of ELM, unknown upper bound values of approximation errors and external disturbances can be online estimated through parameter adaptive laws by using Lyapunov stability analysis, so that semi-global uniform ultimare boundedness of all signals in the closed-loop system can be guaranteed. Finally, simulation results show the effectiveness of the proposed adaptive ELM control.