ADAPTIVE TRACKING CONTROL OF MIMO UNCERTAIN NONLINEAR SYSTEMS BASED ON ELM

Xinyou Wang ； Yongqiang Nai ； Jun Li

《電機工程學刊》 26卷2期 (2019/04) Pp. 45-55

https://doi.org/10.6329/CIEE.201904_26(2).0001

摘要

Based on extreme learning machine (ELM), an adaptive neural control method for a class of uncertain continuous-times multiple-input-multiple-output (MIMO) affine nonlinear dynamic systems is developed. The ELM for signal-hidden layer feedforward neural networks (SLFNs) can analytically determine the output weights of SLFNs and randomly choose the parameters of hidden nodes, which provide good generalized performance at extremely fast learning speed. In the proposed control, ELM is employed to approximate the plant's unknown nonlinear functions and additive robustifying control terms are used to compensate for approximation errors. To obtain an adaptive controller not depending on any parameter initialization conditions and to relax the requirement of bounding parameter values, all parameter adaptive laws for the controller and robustifying control term are derived based on Lyapunov stability analysis so that semi-global stability of the closed-loop system can be guaranteed and the tracking error can asymptotically converge to a small neighborhood of around zero. The performance of the proposed approach, which is compared with that of the existing methods such as Radial basis function (RBF) neural adaptive controller under the same condition, is demonstrated through simulation example. Experiment results confirm that the designed ELM controller shows better performance validity.

關鍵字

Adaptive neural control ； Extreme learning machine ； Signal-hidden layer feedforward neural networks ； Multiple-input-multiple-output ； Nonlinear dynamic systems