Fuzzy Logic Speed Controller Research Articles

Direct torque control of doubly star induction machine fed by voltage source inverter using type-2 fuzzy logic speed controller

Direct torque control of doubly star induction machine fed by voltage source inverter using type-2 fuzzy logic speed controller

Direct torque control of doubly star induction machine fed by voltage source inverter using type-2 fuzzy logic speed controller

Direct torque control of doubly star induction machine fed by voltage source inverter using type-2 fuzzy logic speed controller

Experimental Analysis of Simplified Rules Fuzzy Logic Speed Controller for Wide Speed Range Operations

<p>This paper presents the experimental analysis of simplified rules Fuzzy Logic Speed Controller (FLSC) of Induction Motor drive. The maximum gain of input scaling factor, FLSC is generally limited by the coverage of universe of discoursed (UoD). Thus, to further increase the input gain scaling factor, the outer membership function need to be increased. This analysis covers various values in the range of UoD values from [-1,1] to [-5,5] for the wide speed range operations from low to rated speed ranges. The FLSC is employed to the indirect Field Oriented Control method fed by a voltage source inverter. Simulation and experimental verification is done by using Matlab/Simulink and dSPACE 1103 controller experimental rigs respectively. Based on the results, speed performance behaviours are improved over the wide speed range operations in term of rise time and setting time. The tuning approached is simple without additional algorithm for faster and more accurate response.</p>

High-precision speed control of four-phase switched reluctance motor fed from asymmetric power converter

Switched reluctance motors (SRMs) are used in the industry due to their simple motor design construction, the susceptibility of running at wide speed range, high torque to inertia ratio, low cost, and high reliability. In this paper, the SRM drive system is fed from asymmetric four-phase power converter (APC) using soft switching technique. This paper presents a proposed precise speed controller (PSC) for a four-phase 75 KW 8/6 SRM. The performance of the PSC is evaluated and compared with PI and fuzzy logic (FLC) speed controllers. This comparison is based on the values of the rise time, settling time, overshoot, undershoot, speed error in the steady state, and torque ripples percentage of the three speed controllers. The robustness of the proposed PSC is validated in such cases e.g. mechanical parameter disparities, and external load disturbances. The four-phase SRM responses of motor speed versus speed command, speed error, four-phase currents, SRM reference current, and electromagnetic torque are investigated using MATLAB/SIMULINK.

Direct Torque Control of Doubly Star Induction Motor Using Fuzzy Logic Speed Controller

<p>This paper presents the simulation of the control of doubly star induction<br />motor using Direct Torque Control (DTC) based on Proportional and Integral<br />controller (PI) and Fuzzy Logic Controller (FLC). In addition, the work<br />describes a model of doubly star induction motor in α-β reference frame<br />theory and its computer simulation in MATLAB/SIMULINK®.The structure<br />of the DTC has several advantages such as the short sampling time required<br />by the TC schemes makes them suited to a very fast flux and torque<br />controlled drives as well as the simplicity of the control algorithm.the<br />general- purpose induction drives in very wide range using DTC because it is<br />the excellent solution. The performances of the DTC with a PI controller and<br />FLC are tested under differents speeds command values and load torque.</p>

DIRECT TORQUE CONTROL OF MULTI-PHASE INDUCTION MOTOR WITH FUZZY LOGIC SPEED CONTROLLER

The paper presents the Direct Torque Control with Space Vector Modulation (DTC-SVM) of seven-phase induction motor with Fuzzy Logic Speed Controller. The mathematical model of the seven-phase squirrel-cage induction motor and chosen methods of Space Vector Modulation have been presented. Simulation studies of the DTC-SVM with Fuzzy Logic speed controller have been carried out and the results of simulation studies have been presented and discussed. The author original contribution includes analysis and studies of considered control method of seven-phase induction motor.

Fuzzy logic field-oriented control of an induction motor and a permanent magnet synchronous motor for hybrid/electric vehicle traction applications

This paper presents a fuzzy logic field-oriented control scheme capable of controlling a hybrid/electric vehicle using a variety of traction motor topologies. The control scheme uses fuzzy logic controllers created in MATLAB/Simulink to control the vehicle's speed, the motor's current and the motor's flux which can be applied to an induction motor or a permanent magnet synchronous motor. A comparison between the fuzzy logic controllers and their PID counterpart's performance is also given. The fuzzy logic speed controllers provide comparable accelerator pedal and brake pedal angles to a person controlling the vehicle, leading to realistic vehicle dynamics. The simulations show that the fuzzy logic current and flux controllers can be used on any motor topology and power rating. This control scheme can be used during the design process of a hybrid or electric vehicle without the need for re-tuning whenever the motor configuration changes.

Fuzzy logic field-oriented control of an induction motor and a permanent magnet synchronous motor for hybrid/electric vehicle traction applications

This paper presents a fuzzy logic field-oriented control scheme capable of controlling a hybrid/electric vehicle using a variety of traction motor topologies. The control scheme uses fuzzy logic controllers created in MATLAB/Simulink to control the vehicle's speed, the motor's current and the motor's flux which can be applied to an induction motor or a permanent magnet synchronous motor. A comparison between the fuzzy logic controllers and their PID counterpart's performance is also given. The fuzzy logic speed controllers provide comparable accelerator pedal and brake pedal angles to a person controlling the vehicle, leading to realistic vehicle dynamics. The simulations show that the fuzzy logic current and flux controllers can be used on any motor topology and power rating. This control scheme can be used during the design process of a hybrid or electric vehicle without the need for re-tuning whenever the motor configuration changes.

Gravitational search optimization approach to improve fuzzy logic speed controller for induction motor drive

Gravitational search optimization approach to improve fuzzy logic speed controller for induction motor drive

Fuzzy logic speed control for the engine of an air-powered vehicle

To improve the condition of air and eliminate exhaust gas pollution, this article proposes a compressed air power system. Instead of an internal combustion engine, the automobile is equipped with a compressed air engine, which transforms the energy of compressed air into mechanical motion energy. A prototype was built, and the compressed air engine was tested on an experimental platform. The output torque and energy efficiency were obtained from experimental results. When the supply pressure was set at 2 MPa and the speed was 420 r min−1, the output torque, the output power, and the energy efficiency were 56 N m, 1.93 kW, and 25%, respectively. To improve the efficiency of the system, a fuzzy logic speed control strategy is proposed and simulated. The experimental study verified that the theoretical evaluation of the system was reasonable, and this research can be referred to as the design and control of air-powered vehicles.

A Novel Quantum-Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive

This paper presents a novel lightning search algorithm (LSA) using quantum mechanics theories to generate a quantum-inspired LSA (QLSA). The QLSA improves the searching of each step leader to obtain the best position for a projectile. To evaluate the reliability and efficiency of the proposed algorithm, the QLSA is tested using eighteen benchmark functions with various characteristics. The QLSA is applied to improve the design of the fuzzy logic controller (FLC) for controlling the speed response of the induction motor drive. The proposed algorithm avoids the exhaustive conventional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive input and output MFs are implemented in the fuzzy speed controller design to formulate the objective functions. Mean absolute error (MAE) of the rotor speed is the objective function of optimization controller. An optimal QLSA-based FLC (QLSAF) optimization controller is employed to tune and minimize the MAE, thereby improving the performance of the induction motor with the change in speed and mechanical load. To validate the performance of the developed controller, the results obtained with the QLSAF are compared to the results obtained with LSA, the backtracking search algorithm (BSA), the gravitational search algorithm (GSA), the particle swarm optimization (PSO) and the proportional integral derivative controllers (PID), respectively. Results show that the QLASF outperforms the other control methods in all of the tested cases in terms of damping capability and transient response under different mechanical loads and speeds.

Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds.

Speed Control of Doubly Fed Induction Motor

In this work the performancecomparison study of the proportional integral and fuzzy logic speed controllers which used for the speed control of doubly fed induction machine is presented. For a long time, electromechanical systems used the squirrel-cage induction motors, as main actuators, however, doubly fed induction motors, present of estimable advantages at variable speed drive. From that, the performances of conventional speed controllers are sensitive to parameter variations of the motor. So, in this paper, the analyze of performances of speed fuzzy logic controller is presented. The simulation results showed that the fuzzy logic controller ensure the best dynamic performances in rotor resistance and load variations.

A Comparison between Fuzzy-PSO Controller and PID-PSO Controller for Controlling a DC Motor

The Direct current motors are in different types and there are several methods for controlling of their speed. In this paper two ways for speed controlling suggested. First a fuzzy logic speed controller for DC motor is designed and it’s parameter calculated by Particle Sward Optimization (PSO). The speed controller designed according to fuzzy rules, then for having better performance, the controller optimized with PSO. Secondly a PID controller that it’s parameter find by PSO, is used for speed controlling of a DC motor. At the end the performance of fuzzy logic controller compared with PID controller. The results show that fuzzy-PSO controller has better performance.

Improved brushless DC motor speed controller with digital signal processor

Brushless motors are used in many applications owing to their advantages. In most of the applications, conventional control methods with a hysteresis band (HB) controller are used. An improved speed and current control scheme for brushless DC motors with a trapezoidal shape back EMF is presented. Instead of the conventional HB and proportional-integral (PI) controllers, a fuzzy logic controller that is independent of motor equations and based on expert knowledge has been employed for current and also for speed regulation; thereby, the disadvantages of the HB are eliminated and the overall performance of the controller is enhanced. Experimental studies have been carried out with a TMS320F2812 digital signal processor. The presented control scheme has been validated through comparative experiments with the fuzzy logic speed and HB current controller. The results show that the proposed control scheme operates satisfactorily and provides a constant switching frequency.

Design and FPGA-implementation of an improved adaptive fuzzy logic controller for DC motor speed control

This paper presents an improved adaptive fuzzy logic speed controller for a DC motor, based on field programmable gate array (FPGA) hardware implementation. The developed controller includes an adaptive fuzzy logic control (AFLC) algorithm, which is designed and verified with a nonlinear model of DC motor. Then, it has been synthesised, functionally verified and implemented using Xilinx Integrated Software Environment (ISE) and Spartan-3E FPGA. The performance of this controller has been successfully validated with good tracking results under different operating conditions.

High-performance direct torque and flux control of an induction motor drive using sliding-mode and fuzzy logic speed controllers

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.

Comparative Study of Conventional, Fuzzy Logic and Neural PID Speed Controllers with Torque Ripple Minimization for an Axial Magnetic Flux Switched Reluctance Motor

Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high ripple torque is obtained. In order to reduce this ripple, a control strategy with modified current shapes is proposed. A workbench consisting of a machine prototype and the control system based on a microcontroller was built. These controllers were: a conventional PID, a fuzzy logic PID and a neural PID type. From experimental results, the effective reduction of the torque ripple was confirmed and the performance of the controllers was compared.

A Fuzzy Logic Speed Controller for Separately Excited Dc Motor and Its Comparison with PID Speed Controller

DC motors are widely used in industrial and household applications as they provide benefits of high performance response, high efficiency, high torque and lower volume. This paper proposes the idea of implementing fuzzy logic controller to control the speed of separately excited DC motor. It provides an overview of performance with PID controller and fuzzy logic controller. Though PID controller improves both transients and steady state response characteristics with reduced rise time and steady state error, yet it has limitations of tuning its parameters and unsatisfactory control characteristics. Fuzzy logic can be described simply as computing words and can be designed without knowing the exact mathematical model of the process. Initially these controllers are designed and then tuned to analyze the performance of DC motor. Then the speed response of fuzzy logic is compared with the response achieved by PID controller. The experimental results prove that the control characteristic with fuzzy logic is better than that with the conventional PID controller. The modeling of separately excited DC motor and implementation of the two controllers is performed on software MATLAB/SIMULINK. An error detector compares a feedback signal which is the function of the output response, with the reference input. The variation between these two signals gives an error which is then sent to controller. The controller alters the conditions in the plant to reduce the original error. PID-controllers are extensively used in industrial applications due to reduced number of tuned parameters. In the PID controller, an appropriate proportional gain (Kp), integral gain (Ki), and differential gain (Kd) are adjusted to achieve the optimal control performance. The speed control with PID controller algorithm is simple, stable, easy adjustment and high reliable. However, most industrial processes include different degrees of nonlinear, parameter variability and uncertainty of mathematical model of the system. Thus tuning of parameters of PID controller is very difficult and lacks robustness. It is difficult to achieve the optimal state under field conditions in the actual production. Fuzzy Logic is a logical system having multiple logic values. However it is almost synonymous with the theory of fuzzy sets. The theory of fuzzy sets deal with the classes of objects with blunt boundaries in which membership is a matter of degree. Fuzzy Logic is the expedient way to map input space to an output space.

Type-1 and Type-2 Fuzzy Logic and Sliding-Mode Based Speed Control of Direct Torque and Flux Control Induction Motor Drives – A Comparative Study

Abstract In this research study, the performance of direct torque and flux control induction motor drive (IMD) is presented using five different speed control techniques. The performance of IMD mainly depends on the design of speed controller. The PI speed controller requires precise mathematical model, continuous and appropriate gain values. Therefore, adaptive control based speed controller is desirable to achieve high-performance drive. The sliding-mode speed controller (SMSC) is developed to achieve continuous control of motor speed and torque. Furthermore, the type-1 fuzzy logic speed controller (T1FLSC), type-1 fuzzy SMSC and a new type-2 fuzzy logic speed controller are designed to obtain high performance, dynamic tracking behaviour, speed accuracy and also robustness to parameter variations. The performance of each control technique has been tested for its robustness to parameter uncertainties and load disturbances. The detailed comparison of different control schemes are carried out in a MATALB/Simulink environment at different speed operating conditions, such as, forward and reversal motoring under no-load, load and sudden change in speed.