Résumé: Photovoltaic (PV) arrays are subject to various types of environmental disturbances and component-related faults that affect their normal operation and result in a considerable energy loss. The nonlinear current-voltage (I-V) characteristic curve of the PV array prevents the detection and isolation of the faults and also makes the tracking of the maximum power operating point (MPP) more difficult. Fault detection and identification (FDI) techniques methods have been proposed to detect the presence of faults and isolate them. Many maximum power point tracking (MPPT) methods have been proposed to find the best operating point in the presence of disturbed environmental conditions. However, existing FDI methods do not consider the tracking of the MPP in faulted operating conditions, and available MPP tracking methods do not consider the occurrence of faults in the PV system. The objective of this study is to propose a fault-tolerant control (FTC) strategy to detect the presence of abnormal operating conditions and reconfigure the MPPT procedure to search for the new suboptimal operating point. The FDI method is based on monitoring the PV panel generated power for the presence of abrupt changes; the MPPT reconfiguration is based on a combination between Incremental Conductance (IncCond) Algorithm and an Improved Current-based Particle Swarm Optimization (ICPSO) tracking technique. Simulation and experimental results show an excellent performance of the proposed FTC method in the presence of various types of faults.

Résumé: This paper describes a model predictive controller design to regulate the greenhouse micro-climate, where the controller outputs are computed to optimise the future behaviour of the greenhouse's environment, concerning the setpoint accuracy of the internal temperature and humidity described by Takagi-Sugeno (T-S) model. Modelling procedure is based on two steps. First, the identification of the antecedent part where local linear models are valid using the well-known fuzzy C-means clustering algorithm. Then, recursive least squares (RLS) algorithm is used for consequent part parameters adaptation. An adaptive T-S fuzzy model is considered within the control scheme for prediction of the future greenhouse behaviour. The main way of controlling the greenhouse micro-climate is to use heating and ventilation to regulate both internal temperature and humidity. The simulation results show that the proposed approach maintains successfully both temperature and humidity within the greenhouse around the desired set points in the presence of disturbances. The simulation results are compared between MPC controller based on T-S fuzzy model and MPC based on a single linear model.

Résumé: Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.

Résumé: This paper proposes a new Takagi–Sugeno (T–S) fuzzy model-based maximum power tracking controller to draw the maximum power from a solar photovoltaic (PV) system. A DC–DC boost converter is used to control the output power from the PV panel. Based on the T–S fuzzy model, the fuzzy maximum power point tracking controller is designed by constructing fuzzy gain state feedback controller and an optimal reference model for the optimal PV output voltage, which corresponds actually to maximum power point (MPP). A comparative study with the two base-line controllers of perturb and observe, and the incremental conductance shows that the proposed controller offers fast dynamic response, much less oscillation around MPP, and superior performance.

Résumé: This paper presents a fuzzy tracking control strategy for wind energy conversion system (WECS) using a permanent magnet synchronous generator based variable speed wind turbine (PMSG-WT). The main contribution is to develop a new Takagi-Sugeno fuzzy tracking controller capable to drive the PMSG-WT system to track an optimal reference model maximizing the amount of electrical power extracted from wind energy over a significant wide range of weather conditions. The design procedure can be summarized in two stages: i) Construct the T-S fuzzy controller by using the PMSG-WT model and calculate its matrix gains by solving a set of linear matrix inequalities (LMIs). ii) Construct the nonlinear tracking controller and the optimal reference model according to the optimal rotor speed. Simulations on PMSG- WT model and comparison with baseline PI controller show that the wind turbine plant can be controlled effectively at different operating regions by this scheme.

Résumé: The aim of this paper is to present a new fuzzy tracking strategy for a permanent magnet synchronous machine (PMSM) by using Takagi-Sugeno models (T-S). A feedback-based fuzzy control with h-infinity tracking performance and a concept of virtual reference model are combined to develop a fuzzy tracking controller capable to track a reference signal and ensure a minimum effect of disturbance on the PMSM system. First, a T-S fuzzy model is used to represent the PMSM nonlinear system with disturbance. Next, an integral fuzzy tracking control based on the concept of virtual desired variables (VDVs) is formulated to simplify the design of the virtual reference model and the control law. Finally, based on this concept, a two-stage design procedure is developed: i) determine the VDVs from the nonlinear system output equation and generalized kinematics constraints ii) calculate the feedback controller gains by solving a set of linear matrix inequalities (LMIs). Simulation results are provided to demonstrate the validity and the effectiveness of the proposed method.

Résumé: Face and emotion recognition is still an open and very challenging problem. This paper presents a system FEAST which is an intelligent control system of Smart Tablets. It involves manipulating user sessions to adapt the working environment to his emotional state. First, a face detection followed by face and emotion recognition is performed, then a profile change is made basing on the obtained results. The face detection is based on skin color and geometric moments and face recognition is done by merging two features spaces, namely, Zernike moments and EAR-LBP. A feature selection technique reducing the parameter space size is applied. The same parameters are used for the emotion recognition.

Résumé: In this study the Fuzzy Robust Principal Component Analysis (FRPCA) method is used to monitor a biological nitrogen removal process, performances of this method are then compared with classical principal component analysis. The obtained results demonstrate the performances superiority of this robust extension compared with the conventional one. In this method fuzzy variant of PCA uses fuzzy membership and diminish the effect of outliers by assigning small membership values to outliers in order to make it robust. For the purpose of fault detection, the SPE index is used. Then the fault localization by contribution plots approach and SVI index are exploited.

Résumé: Biological processes are complex, nonlinear uncertain systems and the state variables are not all available for measurement. For that, fuzzy modeling and state estimations are powerful methods for the control and diagnosis of these kind of systems. In this paper, we present some results obtained from the design of fuzzy controller and fuzzy sliding mode observer for an anaerobic digestion system.