Intelligent control of induction motors

This thesis presents the development and implementation of an integral field oriented intelligent control for an induction motor (IM) drive using Fuzzy Logic Controller (FLC), and an Artificial Neural Network (ANN), employing a finite element controller and making use of a Proportional Integral (PI) adaptive controller as well. An analytical model of an induction motor drive has been developed. In order to prove the superiority of the proposed controller, the performance of this controller is compared with conventional PI-based IM drives. The performance of the proposed IM drive is investigated extensively at different operating conditions in simulation. The proposed adaptive PI-based speed controller’s performance is found to be robust and it is a potential candidate for high performance industrial drive applications. The novel work focuses on using a Finite Element Controller map (FECM) to manipulate adaptive controllers for motor control drives. A digital signal processing (DSP) board DS1104 and laboratory induction motor were used to implement the complete vector control scheme. The test results have been compared with simulated results at different dynamic operating conditions. The effectiveness of this control scheme has been evaluated, and it has been found to be more efficient than the conventional PI controller

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

Development of Fuzzy Applications for High Performance Induction Motor Drive

This chapter develops a sliding mode and fuzzy logic-based speed controller, which is named adaptive fuzzy sliding-mode controller (AFSMC) for an indirect field-oriented control (IFOC) of an induction motor (IM) drive. Essentially, the boundary layer approach is the most popular method to reduce the chattering phenomena, which leads to trade-off between control performances, and chattering elimination for uncertain nonlinear systems. For the proposed AFSMC, a fuzzy system is assigned as the reaching control part of the fuzzy sliding-mode controller so that it improves the control performances and eliminates the chattering completely despite large and small uncertainties in the system. A nonlinear adaptive law is also implemented to adjust the control gain with uncertainties of the system. The adaptive law is developed in the sense of Lyapunov stability theorem to minimize the control effort. The applied adaptive fuzzy controller acts like a saturation function in the thin boundary layer near the sliding surface to guarantee the stability of the system. The proposed AFSMC-based IM drive is implemented in real-time using digital signal processor (DSP) board TI TMS320F28335. The experimental and simulation results show the effectiveness of the proposed AFSMC-based IM drive at different operating conditions such as load disturbance, parameter variations, etc

African vulture optimizer algorithm based vector control induction motor drive system

This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response

FUZZY OPTIMIZATION FOR SPEED CONTROLLER OF AN INDIRECT VECTOR CONTROLLED INDUCTION MOTOR DRIVE USING MATLAB SIMULINK

The aim of this paper is to investigate the performance of a Fuzzy-Logic-Controller (FLC) which is applied on three-phase induction motor drive system for high-performance industrial applications. In this paper, the FLC is used as a speed controller to replace the conventional PI speed controller. The FLC will determine the time of switching on the Space Vector Pulse Width Modulation (SVPWM) inverter that supplies power to drive the induction motor considering variable direct and quadrature axis inductances. The performance of the suggested technique has been simulated and developed using MATLAB/ SIMULINK for dynamic operating condition, such as certain change in command speed, and step change in load. The simulation result shows the feasibility of the proposed technique in order to improve the speed response and low torque ripple on three phase induction motor drive

Speed control of induction motor using fuzzy recursive least squares technique

Este artículo presenta el diseño de un controlador adaptativo, el sistema de control emplea lógica difusa adaptativa, modos deslizantes y es entrenado con la técnica de mínimos cuadrados recursivos. El problema de la variación de parámetros es resuelto con el controlador adaptativo; se utiliza un regulador interno PI con el cual se produce que el control de velocidad del motor de inducción sea realizado por medio de las corrientes de estator en vez de los voltajes. Se usa el modelo del motor en el sistema de coordenadas de flujo orientado del rotor para el desarrollo y prueba del sistema de control.A simple adaptive controller design is presented in this paper, the control system uses the adaptive fuzzy logic, sliding modes and is trained with the recursive least squares technique. The problem of parameter variation is solved with the adaptive controller; the use of an internal PI regulator produces that the speed control of the induction motor be achieved by the stator currents instead the input voltage. The rotor-flux oriented coordinated system model is used to develop and test the control system