Robust Control Based on Input-Output Feedback Linearization for Induction Motor Drive: Real Time Implementation

This chapter proposes a design of hardware architecture of an improved Direct Torque Control (DTC) for a real-time implementation on a Xilinx Field-Programmable Gate Array (FPGA). The first contribution in this chapter consists in combining the DTC with a Space Vector Modulation (SVM) technique and an Input-Output Feedback Linearization (IOFL) approach. In fact, the classical DTC has remarkable performance in terms of fast torque response and less dependence on the system parameters. Despite the cited advantages, the classical DTC is penalized by high torque ripples and inverter-switching-frequency variations. In this context, the SVM is added to the DTC structure in order to keep the switching frequency constant and to reduce ripples. Furthermore, the nonlinear IOFL is proposed to achieve a decoupled flux and torque control. The novel structure is named in this chapter as DTC-IOFL-SVM. Moreover, this chapter presents a hardware implementation of the suggested DTC-IOFL-SVM strategy utilization. The hardware implementation is chosen in order to reduce the sampling period of the system thanks to the parallel processing of the FPGA. In order to demonstrate the performance of the FPGA implementation of the proposed DTC-IOFL-SVM, numerous simulation results are presented using the Xilinx system generator under a Matlab/Simulink

Enhanced Intelligent Closed Loop Direct Torque and Flux Control of Induction Motor for Standalone Photovoltaic Water Pumping System

This paper aims to search for a high-performance low-cost standalone photovoltaic water pumping system (PVWPS) based on a three-phase induction motor (IM). In order to control the IM, a fuzzy direct torque control (FDTC) is proposed in this paper for overcoming the limitations of the conventional direct torque control (CDTC). In fact, the CDTC suffers from several problems such as torque ripples, current distortion, and switching frequency variations. These problems can be solved with the proposed FDTC. To ensure high performance of the PVWPS, the reference torque is generated using a fuzzy speed controller (FSC) instead of a conventional proportional integral speed controller. In order to extract the maximum amount of power, the proposed maximum power point tracking controller is based on variable step size perturb and observe to surmount the weakness of the conventional perturb and observe technique. The performance of the proposed FDTC based on the FSC under variable climatic conditions is demonstrated by digital simulation using Matlab/Simulink. The obtained results show the effectiveness of the suggested FDTC based on the FSC compared with the CDTC in terms of pumped water, reduction in flux and torque ripple, diminution of losses, and decrease in the stator current harmonic

Enhanced Intelligent Closed Loop Direct Torque and Flux Control of Induction Motor for Standalone Photovoltaic Water Pumping System

This paper aims to search for a high-performance low-cost standalone photovoltaic water pumping system (PVWPS) based on a three-phase induction motor (IM). In order to control the IM, a fuzzy direct torque control (FDTC) is proposed in this paper for overcoming the limitations of the conventional direct torque control (CDTC). In fact, the CDTC suffers from several problems such as torque ripples, current distortion, and switching frequency variations. These problems can be solved with the proposed FDTC. To ensure high performance of the PVWPS, the reference torque is generated using a fuzzy speed controller (FSC) instead of a conventional proportional integral speed controller. In order to extract the maximum amount of power, the proposed maximum power point tracking controller is based on variable step size perturb and observe to surmount the weakness of the conventional perturb and observe technique. The performance of the proposed FDTC based on the FSC under variable climatic conditions is demonstrated by digital simulation using Matlab/Simulink. The obtained results show the effectiveness of the suggested FDTC based on the FSC compared with the CDTC in terms of pumped water, reduction in flux and torque ripple, diminution of losses, and decrease in the stator current harmonic

Experimental validation of an advanced metaheuristic algorithm for maximum power point tracking of a shaded photovoltaic system: A comparative study between three approaches

Under partially shaded conditions, the power–voltage characteristic curve of the Photovoltaic System (PVS) presents more than one peak, so the Global Maximum Power Point (GMPP) cannot be detected using the conventional Maximum Power Point Tracking (MPPT) algorithms, such as the Perturb-and-Observe (P&O) algorithm. In order to overcome the limitations of the conventional MPPT algorithms, this paper suggests a metaheuristic MPPT called the Crow Search Algorithm (CSA) for the performance optimization of a standalone PVS. The CSA algorithm has the capability of attenuating the negative effects of the partial shading on the performance of the PVS by the accurate detection of the GMPP. The principle of the latter algorithm uses the crow skills and behaviors in the process of locating places to hide its food. Relative to other metaheuristic methods, the CSA utilizes only two tuning parameters that combine between simplicity of implementation and good efficiency. The simulation and experimental results under partial shading applications demonstrates the better performance of the suggested CSA algorithm compared to the particle swarm optimization and P&O algorithms. In fact, the comparison is carried out in terms of high efficiency, good accuracy, low convergence time and simplicity of implementation. Indeed, the proposed CSA-based MPPT approach extracts the maximum power produced by the PVS with an estimated average efficiency of 99.87%, whereas the PSO and P&O methods record average efficiencies of 99.39% and 95.23%, respectively. Furthermore, as compared to the PSO and P&O MPPT methods, the suggested CSA-based MPPT approach reduces convergence time by an average of 48.41% and 49.63%, respectively

FPGA-Based Implementation Direct Torque Control of Induction Motor

This paper proposes a digital implementation of the direct torque control (DTC) of an Induction Motor (IM) with an observation strategy on the Field Programmable Gate Array (FPGA). The hardware solution based on the FPGA is caracterised by fast processing speed due to the parallel processing. In this study the FPGA is used to overcome the limitation of the software solutions (Digital Signal Processor (DSP) and Microcontroller). Also, the DTC of IM has many drawbacks such as for example; The open loop pure integration has from the problems of integration especially at the low speed and the variation of the stator resistance due to the temperature. To tackle these problems we use the Sliding Mode Observer (SMO). This observer is used estimate the stator flux, the stator current and the stator resistance. The hardware implementation method is based on Xilinx System Generator (XSG) which a modeling tool developed by Xilinx for the design of implemented systems on FPGA; from the design of the DTC with SMO from XSG we can automatically generate the VHDL code. The model of the DTC with SMO has been designed and simulated using XSG blocks, synthesized with Xilinx ISE 12.4 tool and implemented on Xilinx Virtex-V FPGA

Robust Variable Structure Control Approach of Two Series-Connected Five-Phase PMSMs Under Healthy and Faulty Operation Modes

In this paper a robust vector control method based on second order Super Twisting Sliding Mode Controllers (STSMCs) for two series-connected Five-Phase Permanent Magnet Synchronous Motors (FP-PMSMs) supplied by a single five-leg inverter is suggested. The vector control method of the two series-connected FP-PMSMs is based on six regulation loops for currents and speeds that are usually based on proportional integral controllers. Thus, the first aim of this paper is to replace the proportional integral controllers by the proposed second order STSMCs in order to enhance the control system performance in terms of robustness under uncertainties, external disturbances and tracking accuracy. Indeed, the second order STSMCs are proposed in order to overcome the limitations of the proportional integral controllers in terms of sensitivity against the variations in FP-PMSMs parameters and load disturbances and to overcome the first order sliding mode control chattering problem. However, the suggested second order STSMCs require information about the load torques applied to the two FP-PMSMs, which are estimated using sliding mode load torque observers hence reducing the cost and maintenance rate of the electromechanical system due to the elimination of load sensors. Our simulation studies conducted in MATLAB/Simulink check whether the proposed topology of the two series-connected FP-PMSMs controlled by the suggested vector control based on second order STSMCs can regulate the speed at the same time under various conditions. These conditions include load disturbances, parameter variations in both machines, and disturbances resulting from the opening of a phase. Our objective is to demonstrate the excellent robustness of the suggested second order STSMCs based vector control approach in terms of independent speed control of the two FP-PMSMs even at low or reversed speeds, compared with the conventional proportional integral control strategy. In this comparative study, various performance indices are used to demonstrate the robustness of the proposed vector control method based on second order STSMCs control strategy in comparison to the vector control method based on proportional integral controllers

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience