PID Control for Takagi-Sugeno Fuzzy Model

In this chapter, we deal with the problem of controlling Takagi-Sugeno (TS) fuzzy model by PID controllers using the particle swarm optimization (PSO). Therefore, a new algorithm is proposed. This algorithm relies on the use of a new objective function taking into account both the performance indices and the error signal. The advantages of this approach are discussed through simulations on a numerical example

Brushless Three-Phase Synchronous Generator Under Rotating Diode Failure Conditions

International audienceIn brushless excitation systems, the rotating diodes can experience open- or short-circuits. For a three-phase synchronous generator under no-load, we present theoretical development of effects of diode failures on machine output voltage. Thereby, we expect the spectral response faced with each fault condition, and we propose an original algorithm for state monitoring of rotating diodes. Moreover, given experimental observations of the spectral behavior of stray flux, we propose an alternative technique. Laboratory tests have proven the effectiveness of the proposed methods for detection of fault diodes, even when the generator has been fully loaded. However, their ability to distinguish between cases of diodes interrupted and short-circuited, has been limited to the no-load condition, and certain loads of specific natures

SEIG-based Wind Turbine Condition Monitoring using Stray Flux Instantaneous Frequency Estimation

International audienceFor economic and environmental reasons, wind turbines are becoming a potential renewable power source that could replace conventional fossil-fuelled plants. In remote areas where the power grid is unavailable, these wind plants may be equipped with self-excited induction generators. Order to maximize their productivity, the generators condition has to be continually monitored. For this purpose, many processing techniques have been interested to the analysis of fluently known signals such that vibration, ultrasound, acoustic emission, temperature, electrical amounts, etc. In this work, we present an innovative approach for monitoring the drive speed of such generator. The proposed technique is based on estimation of the instantaneous frequency related to the signal stemming from a stray flux sensor. Experimental investigations conducted on a laboratory test-rig have shown promising results in terms of speed monitoring by the employ of a low-cost sensor

Adaptive proportional-integral fuzzy logic controller of electric motor drive

This paper presents the indirect field vector control of induction motor (IM) which is controlled by an adaptive Proportional-Integral (PI) speed controller. The proposed solution can overcome the rotor resistance variation, which degrades the performance of speed control. To solve this drawback, an adaptive PI controller is designed with gains adaptation based on fuzzy logic in order to improve the performances of IM with respect to parameters variations, particularly the rotor resistance (Rr). The proposed control algorithm is validated by simulation tests. The obtained results show the robustness towards the load torque disturbances and rotor resistance variation of the adaptive Proportional-Integral fuzzy logic control with respect to classical PI control, and adaptive control based on rotor resistance observer

PID Control for Nonlinear Processes

This chapter presents a proportional-integral-derivative (PID) Takagi-Sugeno fuzzy system controller that can be trained by the particle swarm optimization-cuckoo search (PSOCS) technique to control nonlinear multi-input multi-output (MIMO) systems. Instead of the standard methods that are widely used in the literature, the PSOCS is used to adjust all of the PID parameters by the minimization of a given objective function. A nonlinear MIMO system has been selected to be controlled by this controller. The simulation results show the notable control accuracy and generalization ability of this MIMO controller. Finally, a comparative study with a PSO algorithm and CS algorithm shows the superiority of the PSOCS over these two optimization methods in terms of guaranteeing the desired performance

Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou's Curve Analysis and Neural Network-Based Decision

International audienceIn this work we propose an original fault signature based on the Hilbert-Park Lissajou's curve analysis. The performances of the proposed signature were compared to those of the Park Lissajou's curve which is the signature most recently used. The proposed fault signature does not require a long temporal recording, and their processing is simple. This analysis offers an easy interpretation to conclude on the induction motor condition and its voltage supply state. The proposed signature shows its efficiency especially in the case of unloaded machine. The geometrical characteristic of all Hilbert-Park Lissajou's curves are calculated in order to develop the input vector necessary for the pattern recognition tools based on neural network approach with an aim of classifying automatically the various states of the induction motor. This approach was applied to a 1.1 kw induction motor under normal operation and with the following faults: unbalanced voltage, air-gap eccentricity and outer raceway bearing defect

Subspace Identification of Hammerstein Model with Unified Discontinuous Nonlinearity

The main aim of this study is to handle the case where the structures of nonlinear systems are unknown. In the many works, the parametric identification of nonlinear systems represented by Hammerstein model, with discontinuous and asymmetric nonlinearity, considers the structures of the nonlinear and linear blocks are known, especially the nonlinear bloc. To solve this problem, a unified form of nonlinearity representing eight cases of nonlinearities can be used. The parameters of both blocks, linear and nonlinear, are estimated using an iterative subspace approach. More importantly, in an attempt to show the extent to which this method is efficient, we apply it to experimental data obtained from the electropneumatic system. As a result, the numerical and experimental examples confirm a good conditioning and computational efficiency

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods