New Results on PWARX Model Identification Based on Clustering Approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.

Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model

In this paper, the problem of hybrid model predictive control(HMPC) strategy based on fuzzy supervisor for piecewise autoregressive with exogenous input(PWARX) models is addressed. We first represent the nonlinear behavior of the system with a PWARX model. Then, we transform the obtained PWARX model into a mixed logical dynamic(MLD) model in order to apply the proposed predictive control which is able to stabilize such systems along desired reference trajectories while satisfying operating constraints.Finally, we propose to introduce a fuzzy supervisor allowing the readjustment of the HMPC tuning parameters in order to maintain the desired performance. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach.