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Data-based identification and control of nonlinear systems via piecewise affine approximation.
Chow Yin Lai1, Cheng Xiang, Tong Heng Lee
1National University of Singapore Graduate School for Integrative Sciences and Engineering, 117456 Singapore. g0601819@nus.edu.sg
IEEE Transactions on Neural Networks
|December 8, 2011
Summary
This study introduces a method to create accurate piecewise affine (PWA) autoregressive models for nonlinear systems. The developed controller effectively tracks system references, validated by simulations and experiments.
Area of Science:
- Control Systems Engineering
- Nonlinear System Identification
- Computational Mathematics
Background:
- Nonlinear systems are challenging to model and control accurately.
- Piecewise affine (PWA) models offer a flexible structure for approximating nonlinear dynamics.
- Existing methods may lack efficiency or accuracy in PWA model derivation.
Purpose of the Study:
- To propose a novel procedure for obtaining piecewise affine autoregressive exogenous (PWARX) models of nonlinear systems.
- To develop a controller for reference tracking based on the identified PWARX model.
- To validate the accuracy of the PWA approximation and the controller's performance.
Main Methods:
- Estimating parameters of locally affine subsystems using a least-squares-based identification method.
- Determining the partition of the regressor space via neural network or support vector machine classifiers.
- Deriving a controller for reference tracking using the identified PWARX model.
Main Results:
- The proposed algorithm accurately approximates nonlinear systems using PWA models.
- The identified PWARX models capture the system's behavior effectively.
- The designed controller demonstrates good reference tracking performance in simulations and experiments.
Conclusions:
- The developed procedure provides an effective means for identifying PWARX models of nonlinear systems.
- The PWARX modeling approach facilitates the design of effective controllers for nonlinear systems.
- The study confirms the practical applicability of PWA models in system identification and control.
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