Simplified Fuzzy Logic Speed Controller for Vector Controlled Permanent Magnet Synchronous Motor Drives

This paper presents a fuzzy logic controller for the closed loop control of BLDC motor. The fuzzy logic controller is developed using a fuzzy logic toolbox in MATLAB. Also here a single current sensor technique is proposed for closed loop current control. For closed loop current control of BLDC motor, the motor phase currents are measured using current sensors. These sensors are expensive and the use of different current sensors can cause undesirable imbalance in phase currents due to differences in current sensor sensitivities. These drawbacks can be avoided by using a single current sensor placed on the DC link. Here an algorithm is presented to obtain the phase current values from the DC link current. The proposed system was simulated using MATLAB/Simulink, with PI and fuzzy logic speed controllers. The simulation result shows that, the fuzzy logic controller gives better performance than PI controller. The hardware implementation of the fuzzy logic controller is done using PIC16F877A. The hardware results show that the fuzzy logic controller gives a smooth speed control.

This paper deals with the dSPACE DS1103 based implementation of a simplified fuzzy logic speed controller (Simplified FLSC) for vector controlled permanent magnet synchronous motor (PMSM) drives. The simplification of FLSC obtains minimization and adjustment of the membership function used by each fuzzy variable for the drive system. The mathematical model of PMSM drives has been simulated in the MATLAB environment using Simulink and Fuzzy Logic Toolbox to analyze the performance of the drive system. The simulated results are then validated with the experimental investigation. For experimentation, the FLSC, co-ordinate transformation and hysteresis current controller (HCC) have been implemented in MATLAB environment with Simulink, Fuzzy Logic Toolbox and Real-time interface. Meanwhile, the hardware implementation consisting of dSPACE digital signal processor, voltage source inverter, resolver-to-linear DC converter, current sensors and PMSM equipped with speed resolver. The PMSM drive system has been tested under various operating conditions and load disturbances.

In this paper, a fuzzy logic speed controller in combination with feedforward fuzzy logic current controller is designed and modelled to control a permanent magnet synchronous motor (PMSM) drive for high performance drive applications. The fundamentals of fuzzy logic based control algorithms are illustrated in brief. The detailed performance of PMSM drive is studied applying various mechanical disturbances such as starting, speed reversal, flux weakening and load perturbations.

An improved simplified rules Fuzzy Logic Speed Controller method applied for induction motor drive

Direct Torque Control (DTC) technique is one of the most effective strategies for an electric machines. It aims to provide a decoupled control of torque and flux via a simple command structure. The aim of this paper is the study of the problems that can affect the operation of the speed loop for Induction Motor Open-End Winding (IM-OEW) through failures, especially broken bars fault, its impact on the control and operation of the motor. This paper presents a comparative study of the most powerful speed controllers for motor: proportional plus integral (PI) controller and a fuzzy logic (FL) controller. The awaited goal is to evaluate the speed controller is capable to improve the performance of the machine under faulty conditions. The obtained results proved the effectiveness of the DTC strategy with a fast dynamic, better load disturbances rejection and better tracking performance without overshoot when the FL speed controller employed, which ensures robust speed control under broken bars fault.

• Modeling and design of an intelligent control system for electric vehicle applications. • Improvement of direct torque control of DFIM for electric vehicle applications through the use of fuzzy controllers. • Comparative analysis between classical DTC control and DTC based on fuzzy logic of the doubly feed induction motor for electric vehicle applications. Conventional Direct Torque Control (DTC) presents challenges such as significant torque ripples and limited adaptability to load variations. These challenges are exacerbated when applied to electric vehicles (EVs) due to changes in driving speed and the load torque that depends on road conditions. In this context, this work aims to improve the DTC of a Doubly Fed Induction Motor (DFIM) used for electric propulsion. Hysteresis comparators and the control table, responsible for the ripples in conventional DTC, have been replaced by fuzzy logic controllers. Additionally, a fuzzy logic speed controller has been designed to ensure better speed tracking across different ranges (low, medium, and high speed). Simulations performed using Matlab/Simulink allowed for a comparison of the proposed control strategy with conventional DTC. The results show a reduction of 42 % in torque ripples. Furthermore, the proposed control strategy does not show any overshoot compared to the conventional DTC strategy, which shows an overshoot of 1.5 km/h. This improves adaptability to load variations and the precision of dynamic response, optimizing the overall vehicle performance. These improvements result in smoother driving and increased overall efficiency of EVs, demonstrating the practicality and effectiveness of the fuzzy logic control strategy for electric propulsion applications.

Fuzzy logic-based algorithms are widely employed in variable-speed drives. However, although they provide an excellent speed-control performance, they may result in significant current harmonics. These may be reduced by employing a non-linear distribution of fuzzy sets in the membership function of the output variable, albeit with some deterioration in the speed-control performance. A simple adaptive fuzzy logic control algorithm, in which the speed-error threshold may be either fixed or self-tuned, is proposed. Simulations and measurements on vector-controlled permanent-magnet brushless AC drives confirm that the proposed adaptive fuzzy logic control results in an excellent speed-control performance and robustness to parameter variations, while current harmonics are reduced. Its performance is compared with that which is achieved when conventional fuzzy logic and PI speed control are employed.

In this paper, a fault diagnosis method is presented in order to detection the inter-turn short circuit fault in permanent magnet synchronous motor (PMSM) based on the comparative analysis of two types of controllers. This study consists in the sliding mode control (SMC) of the (PMSM) and the two controllers (fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC) by taking account of the presence of inter-turn short circuit fault. This study aims to select the most robust controller against the stator faults, load torque variation and reversing rotation speed. Tests regarding these two controllers associated with the sliding mode control were carried on a PMSM 5KW under to different loads, speed and severity degrees of failures for a functioning in healthy and degraded state. The obtained simulation results confirm the superiority and robustness of AFLC-SMC (insensitive to the load torque and the rotation direction, faster response time, decrease the oscillations, and minimization of THD) compared to FLC-SMC.

This study investigates the use of Genetic Algorithms (GA) to the design and implementation of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

This paper presents an advanced Direct Torque Control (DTC) approach for a Dual Three-Phase Induction Motor (DTPIM) powered by a Direct Power Control (DPC) rectifier. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often face performance issues when motor system parameters vary or exhibit non-linearity. To tackle these challenges, we propose a fuzzy logic-based speed controller for DTC, which enhances adaptability to system dynamics without necessitating a precise mathematical model. The fuzzy logic controller (FLC) is particularly effective in regulating speed under varying load conditions, improving robustness, and minimizing torque ripple. Furthermore, the DPC rectifier enhances power quality by reducing harmonic distortions, maintaining a stable DC link voltage, and improving the power factor. Simulation results obtained using MATLAB/Simulink software demonstrate that the combined DTC-DPC approach with fuzzy logic control delivers a superior dynamic response with minimal overshoot. This framework offers a promising solution for high-performance industrial applications that require precise torque control and stability under fluctuating loads while also supporting sustainable energy practices through improved power efficiency.

This paper studies the use of Genetic Algorithms (GA) in the design of Fuzzy Logic Controllers (FLC) and show how population size, probability of crossover and rate of mutation can effect the performance of the GA. The comparison of various parameters shows that GA is helpful in improving the performance of FLC. A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that the GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

Performance evaluation of a brushless DC (BLDC) motor with conventional PI and Fuzzy Logic based speed controller is presented in this paper. The BLDC motor with non-sinusoidal (trapezoidal) back-electromotive force has been analyzed with both the speed and current controllers. It is the always preferred to have a drive with faster and smoother speed response and reduced ripples in current and torque. The controlling schemes for BLDCM using its Back EMF have been conventionally used into many applications. In this paper, a simple control scheme has been implemented which does not need to know the back emf or shape function and even no complicated calculations. In order to solve the problems associated with conventional PI speed controller, Fuzzy logic speed controller is proposed to reduce starting current, eliminate overshoot in the torque and fast speed response. It is simple in design and eliminates the complex mathematical computation. The effectiveness of proposed system has been validated by simulation results and improved performance of controlling BLDCM. The proposed algorithm gives robust control. The robustness of the proposed algorithm is demonstrated through the MATLAB simulation.

Alternative current motors are generally modelled with constant system parameters. However, parameters of the motor show variations during operation due to complex, unidentified and nonlinear system dynamics. Fuzzy logic controllers are widely used as a solution to overcome this problem. Because, fuzzy logic controllers don’t be affected from model uncertainties of the system to be controlled. In this study, permanent magnet synchronous motor that is one of the alternative current motors is controlled with fuzzy logic controller. All stages of the designed controllers are developed by using unique softwares instead of ready toolboxes. Triangular and trapezoidal membership functions with Mamdani and Larsen inference methods are used in the designed fuzzy logic controllers. Speed control performances of the fuzzy logic controllers have been investigated and compared with each other in the simulation studies. According to the obtained results from simulation studies, it is observed that fuzzy logic controllers designed with Larsen fuzzy inference method and trapezoidal input membership function provides better performance.

In this research study, direct torque and flux control of an Induction Motor Drive (IMD) using Sliding-Mode Speed Controllers (SMSC) and Fuzzy-Logic Speed Controller (FLSC) are presented to achieve high performance of stator current, electromagnetic torque and motor speed in both transient as well as in steady-state conditions. The PI Speed Controller (PISC) gives good steady-state performance, but poor dynamic response. Therefore, the SMSC is considered to achieve continuous control of motor speed and also electromagnetic torque. Furthermore, FLSC is considered to get high performance, dynamic tracking and speed accuracy at various load torque conditions. The performances of each speed controller techniques are tested under various load toque and speed condition for its robustness. A detailed comparison of different control approaches are carried out in a MATALB/Simulink environment at different operating conditions, such as forward and reversal motoring with no-load, load, sudden change in speed and sudden zero speed.

Computer technology, which has been developing very fast in the recent years, can be also fruitfully applied in teaching. For example, the software package Matlab is highly useful in teaching students at Bachelor Programs of Electrical Engineering and Automatics and Robotics. Fuzzy Logic Toolbox of the Matlab package can be used for designing and modelling controllers. Thanks to a large number of pre-defined elements available in the libraries, it is possible to create even highly complicated models of systems without much effort. Fuzzy Logic Toolbox is especially useful for exploring the basic rules of designing fuzzy logic controllers. The rules involve selecting input and output membership functions, determining their location with respect to one another and defining their ranges. When the membership functions are introduced, a rule base is defined and a defuzzification method is selected. For any defuzzification method, a control surface is obtained, which can be modified by changing the rule base and/or the input and output parameters of the membership function.