title: Data-based identification and control of nonlinear systems via piecewise affine approximation.
abstract: The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA autoregressive exogenous (ARX) (autoregressive systems with exogenous inputs) models of nonlinear systems is proposed. Two key parameters defining a PWARX model, namely, the parameters of locally affine subsystems and the partition of the regressor space, are estimated, the former through a least-squares-based identification method using multiple models, and the latter using standard procedures such as neural network classifier or support vector machine classifier. Having obtained the PWARX model of the nonlinear system, a controller is then derived to control the system for reference tracking. Both simulation and experimental studies show that the proposed algorithm can indeed provide accurate PWA approximation of nonlinear systems, and the designed controller provides good tracking performance.
authors.affiliation: National University of Singapore Graduate School for Integrative Sciences and Engineering, 117456 Singapore. g0601819@nus.edu.sg
authors.forename: Chow Yin
authors.initials: CY
authors.lastname: Lai
authors.forename: Cheng
authors.initials: C
authors.lastname: Xiang
authors.forename: Tong Heng
authors.initials: TH
authors.lastname: Lee
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005246:Feedback; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*