C.Y. Lai, C. Xiang, and T.H. Lee (Singapore)
Piecewise Affine System, System Identification, Model Predictive Control
In this paper, a procedure for the identification and con trol of piecewise affine ARX systems is proposed. First, the parameters of the individual subsystems are identified through a least-squares based identification method using multiple models, followed by the estimation of the partition of the regressor space using standard procedures such as neural network classifier or support vector machine classi fier. Having obtained the complete model of the piecewise affine ARX system, a model predictive controller is then derived to control the system for reference tracking. Sim ulation studies show that our algorithm can indeed provide accurate estimates of the plant parameters even in noisy cases, and that the proposed controller provides good track ing performance.
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