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Electric vehicles (EVs) require uninterrupted and safe conditions during operations. Therefore, the diagnostic of power devices and electric motor faults are needed to improve the availability of the system. Hence, fault-tolerant control (FTC), which combines switch fault detection, hardware redundancy and post-fault control, is used. This paper aims to propose an accurate open-phase fault detection and FTC of a direct torque control permanent magnet synchronous motor electrical vehicles by using discrete Fourier-transform phase method.

The main idea is to propose detection and identification of open-phase fault (faulty leg) among three phases voltage source invertor (VSI)-fed permanent magnet synchronous motor drives. Once the faulty leg is detected and isolated, a redundant phase leg insertion, shared by a three-phase VSI, is done by using independent bidirectional TRIAC switches to conduct FTC system. This accurate fault detection significantly improves system availability and reliability. The proposed method of open-phase fault detection and identification is based only on stator phase current measurement.

A novel method is proposed with experimental validation for fault detection, isolation and FTC for a three-phase VSI-fed permanent magnet synchronous motor.

The novel discrete Fourier-transform phase method is proposed to detect an open phase based on the measurement in real time of the instantaneous phase of stator current components in the stationary frame. The experimental implementation is carried out on powerful dSpace DS1104 controller board based on the digital signal processor TMS320F240. The validity of the proposed method has been experimentally verified.

Tunisia has high solar radiation levels, which makes it suitable for the installation of photovoltaic (PV) systems. The design of these kinds of systems is an important step because there are many crucial factors to assess the PV module efficiency such as temperature, module types and solar radiation.This paper aims to give an analysis of the most influencing factor for selecting location. In fact, after estimating the PV panel inclination, the solar radiation and the temperature in “Zarzis” (southeastern of Tunisia), a comparative analysis among the different PV panel types was given. Additionally, to find which technologies are suitable for the climate conditions of this area, it is important to compare the effect of temperature and solar radiation on their performances.

The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor.

A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints.

The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller.

This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

The purpose of this paper is to present a new methodology for identification of the parameters of the local linear Takagi‐Sugeno fuzzy models using weighted recursive least squares. The weighted recursive least squares (WRLS) is sensitive to initialization which leads to no converge. In order to overcome this problem, Euclidean particle swarm optimization (EPSO) is employed to optimize the initial states of WRLS. Finally, validation results are given to demonstrate the effectiveness and accuracy of the proposed algorithm. A comparative study is presented. Validation results involving simulations of numerical examples and the liquid level process have demonstrated the practicality of the algorithm.

A new method for nonlinear system modelling. The proposed algorithm is employed to optimize the initial states of WRLS algorithm in two phases of learning algorithm.

The results obtained using this novel approach were comparable with other modeling approaches reported in the literature. The proposed algorithm is able to handle various types of modeling problems with high accuracy.

In this paper, a new method is employed to optimize the initial states of WRLS algorithm in two phases of the learning algorithm.

This paper aims to design a modified fractional order proportional integral derivative (PID) (FO[PI]^λD^µ) controller based on the principle of fractional calculus and investigate its performance for a class of a second-order plant model under different operating conditions. The effectiveness of the proposed controller is compared with the classical controllers.

The fractional factor related to the integral term of the standard FO[PI]^λD^µ controller is applied as a common fractional factor term for the proportional plus integral coefficients in the proposed controller structure. The controller design is developed using the regular closed-loop system design specifications such as gain crossover frequency, phase margin, robustness to gain change and two more specifications, namely, noise reduction and disturbance elimination functions.

The study results of the designed controller using matrix laboratory software are analyzed and compared with an integer order PID and a classical FOPI^λD^µ controller, the proposed FO[PI]^λD^µ controller exhibit a high degree of performance in terms of settling time, fast response and no overshoot.

This paper proposes a methodology for the FO[PI]^λD^µ controller design for a second-order plant model using the closed-loop system design specifications. The effectiveness of the proposed control scheme is demonstrated under different operating conditions such as external load disturbances and input parameter change.