Jean-Yves Dieulot

A new approach for multimodel identification of complex systems based on both neural and fuzzy clustering algorithms

The multimodel approach was recently developed to deal with the issues of complex systems modeling and control. Despite its success in different fields, it is still faced with several design problems, in particular the determination of the number and parameters of the different models representative of the system as well as the choice of the adequate method of validities computation used for multimodel output deduction. In this paper, a new approach for complex systems modeling based on both neural and fuzzy clustering algorithms is proposed, which aims to derive different models describing the system in the whole operating domain. The implementation of this approach requires two main steps. The first step consists in determining the structure of the model-base. For this, the number of models must be firstly worked out by using a neural network and a Rival Penalized Competitive Learning (RPCL). The different operating clusters are then selected referring to two different clustering algorithms (K-means and fuzzy K-means). The second step is a parametric identification of the different models in the base by using the clustering results for model orders and parameters estimation. This step is ended in a validation procedure which aims to confirm the efficiency of the proposed modeling by using the adequate method of validity computation. The proposed approach is implemented and tested with two nonlinear systems. The obtained results turn out to be satisfactory and show a good precision, which is strongly related to the dispersion of the data and the related clustering method.

Coordinated predictive control in active distribution networks with HV/MV reactive power constraint

This paper presents a new real time centralized Model Predictive Control algorithm for distribution networks. Compared to existing works regarding MPC volt var control, the proposed algorithm controls not only the MV voltages but also the reactive power exchange at the distribution and transmission systems interface. Control of reactive power exchange is a new requirement of the European Network Code on Demand and Connection. The controller adjusts the reactive power of the distributed generators and the voltage reference of HV/MV on load tap changers and capacitor banks. This method was simulated on a 20 kV network taking into account actual technical limitations of distribution networks.

A New Multimodel Approach for Complex Processes Modeling Based on Classification Algorithms: Experimental Validation

Abstract In this paper, a new multimodel approach for complex systems modeling based on classification algorithms is presented. It requires firstly the determination of the model-base. For this, the number of models is selected via a neural network and a rival penalized competitive learning (RPCL), and the operating clusters are identified by using the fuzzy K-means algorithm. The obtained results are then exploited for the parametric identification of the models. The second step consists in validating the proposed model-base by using the adequate method of validity computation. An experimental validation is presented in this paper which shows the efficiency of the proposed approach.

Indirect iterative learning control of Takagi-Sugeno fuzzy systems

Abstract An indirect iterative learning control is proposed to update the gains of a Parallel Distributed Compensation controller when the trajectory is repetitive. The corresponding Takagi-Sugeno fuzzy system, to be controlled, is supposed to exhibit time-varying matrices in the consequent part. When the membership functions are fixed, it will be possible to estimate the consequent system for every time, using different control gains for each trial. Finally, when the system parameters are considered as properly estimated, it will be possible to derive a controller which will not only be stable but will also exhibit good tracking properties.