Data-Driven MFAC for a Class of Discrete-Time Nonlinear Systems With RBFNN

Author

Yuanming Zhu ; Zhongsheng Hou

Author_Institution

Adv. Control Syst. Lab., Beijing Jiaotong Univ., Beijing, China

Volume

25

Issue

5

fYear

2014

fDate

May-14

Firstpage

1013

Lastpage

1020

Abstract

A novel model-free adaptive control method is proposed for a class of discrete-time single input single output (SISO) nonlinear systems, where the equivalent dynamic linearization technique is used on the ideal nonlinear controller. With radial basis function neural network, the controller parameters are tuned on-line directly using the measured input and output data of the plant, when the plant model is unavailable. The stability of the proposed method is guaranteed by rigorous theoretical analysis, and the effectiveness and applicability are verified by numerical simulation and further demonstrated by the experiment on three tanks water level control process.

Keywords

adaptive control; discrete time systems; neurocontrollers; nonlinear control systems; radial basis function networks; stability; RBFNN; data-driven MFAC; discrete-time single input single output nonlinear system; equivalent dynamic linearization technique; measured input and output data; model-free adaptive control method; nonlinear controller; radial basis function neural network; water level control process; Adaptation models; Control systems; Data models; Learning systems; Neurons; Tuning; Vectors; Controller dynamic linearization; data driven control (DDC); model free adaptive control (MFAC); radial basis function; radial basis function.;

fLanguage

English

Journal_Title

Neural Networks and Learning Systems, IEEE Transactions on

Publisher

ieee

ISSN

2162-237X

Type

jour

DOI

10.1109/TNNLS.2013.2291792

Filename

6684279