A PWA model identification method based on optimal operating region partition with the output-error minimization for nonlinear systems |
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Affiliation: | 1. Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA;2. Innovation and Technology Center, Braskem, Pittsburgh, PA 15219, USA;1. Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano 20133, Italy;2. Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom |
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Abstract: | When piecewise affine (PWA) model-based control methods are applied to nonlinear systems, the first question is how to get sub-models and corresponding operating regions. Motivated by the fact that the operating region of each sub-model is an important component of a PWA model and the parameters of a sub-model are strongly coupled with the operating region, a new PWA model identification method based on optimal operating region partition with the output-error minimization for nonlinear systems is initiated. Firstly, construct local data sets from input-output data and get local models by using the least square (LS) method. Secondly, cluster local models according to the feature vectors and identify the parameter vectors of sub-models by weighted least squares (WLS) method. Thirdly, get the initial operating region partition by using a normalized exponential function, which is to partition the operating space completely. Finally, simultaneously determine the optimal parameter vectors of sub-models and the optimal operating region partition underlying the output-error minimization, which is executed by particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed method can improve model accuracy compared with two existing methods. |
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