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This article aims first to propose an improved space vector modulation–direct torque control in order to enhance the induction motor performance using: (1) a super-twisting controller and (2) a novel model reference adaptive system based on a sliding mode observer. Second, due to the complexity of the suggested control algorithm, this article deals also with a hardware implementation on a field-programmable gate array board. Indeed, the field-programmable gate array is mainly chosen to reduce the execution time, thanks to its parallel processing which significantly improves the quality of the control system by reducing the sampling period, and consequently the delays in the control loop. Besides, the super-twisting controller is proposed to enhance the speed regulation loop which is a second-order sliding mode control technique that uses a continuous control law to prevent the chattering phenomenon induced by the first-order sliding mode control technique. Moreover, the high performance control requires information about the rotor speed which can be obtained by a sensor. Generally, the use of the speed sensor increases the system cost and size, and reduces its system reliability. Therefore, a combination between a model reference adaptive system observer and a sliding mode observer is suggested for speed estimation and overcoming the sensitivity of the classical model reference adaptive system observer against uncertainties and stator resistance variations. The performance of the proposed space vector modulation--direct torque control--super-twisting controller with the model reference adaptive system--sliding mode observer algorithm of an induction motor is verified through simulation, hardware co-simulation and experimental validation utilizing a field-programmable gate array Virtex-5-ML507 board. The performance of the suggested sensorless control method is compared also with other recent published schemes. [ABSTRACT FROM AUTHOR]

This paper presents a hardware implementation upon a Field Programmable Gate Array (FPGA) of improved Vector Control Strategies (VCSs) based on a Fuzzy Logic System (FLS) of a grid-connected wind energy conversion system. Usually, the classical VCS is based on fixed-gain Proportional Integral Derivative (PID) controllers, which are known to exhibit limited performance against the nonlinear behavior of wind systems, such as rapid fluctuations of the wind speed and uncertainties of the system parameters. In order to overcome this limitation, an improved VCS based on Fuzzy Gain Scheduling PID controllers (VCS-FGS-PID) is suggested in this work to guarantee good tracking, high accuracy and good robustness under system parameter variations. Indeed, the PID controller gains are tuned, in real-time, by the FLS. In addition, the proposed VCS-FGS-PID methods are implemented on the FPGA in order to reduce the delays and the period of the system of the control loop, thanks to its parallel processing. In fact, the performance of the suggested VCS-FGS-PID approaches are proved by digital simulation with the Xilinx system generator tool under Matlab/Simulink, in addition to an experimental hardware-in-the-loop implementation using the FPGA. The obtained results demonstrate that the proposed VCS-FGS-PID techniques offer better performance as regards good tracking and high accuracy and good robustness against stator resistance variability compared to VCS-PI. [ABSTRACT FROM AUTHOR]

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems. [ABSTRACT FROM AUTHOR]

The conventional Direct Torque Control (DTC) is considered as a powerful approach which is widely used to control induction motors. However, this control approach suffers from several problems like undesirable torque and flux ripples and stator resistance variations, especially at low speed. To overcome these problems, a Variable Structure Control associated with a DTC based on Space Vector Modulation (VSC-DTC-SVM) is proposed in this paper to reduce ripples and enhance the system robustness. Due to the complex scheme of the suggested VSC-DTC-SVM approach, its implementation on software solutions such as microcontrollers and digital signal processors requires a high sampling period. In fact, this period creates delay in applying the inverter switching states which results undesirable ripples and distortions in the torque and the stator current, respectively. This delay is inevitable, and thus the VSC-DTC-SVM performances are affected and the sampling frequency is limited. These limitations in digital control are mainly caused by the calculation speed which depends on the complexity of the algorithm and the serial processing of the software solutions. To cope with this problem, a programmable digital circuit such as the FPGA is chosen to preserve the performances of the VSC-DTC-SVM in spite of its complexity, thanks to its parallel processing. To illustrate this, a hardware implementation of the VSC-DTC-SVM of an induction motor on the FPGA is presented and analyzed. The VSC-DTC-SVM performances in terms of ripples, under stator resistance variation, are presented by a theoretical study, a digital simulation and a hardware co-simulation using an FPGA Virtex 5. [ABSTRACT FROM PUBLISHER]