A High Performance Induction Motor Drive System Using Fuzzy Logic Controller

Use of induction motor (IM) at it's highest efficiency is a big demanding task because of their complex mathematical model and non linear features during saturation. These elements make the control of IM strenuous and call for the use of high performance control algorithm such as field orientation control. This paper introduces a speed controller, particularly at minimum speeds by utilizing Fuzzy logic controller (FLC) for indirect field-oriented control (IFOC) of induction motor (IM) drives fed by a four-switch three-phase (FSTP) inverter. From suggested viewpoint, the IM drive is used with a FSTP inverter as an alternative for the conventional six-switch three-phase (SSTP) inverter. Utter IFOC programme includes the FLC of IM drives with the suggested FSTP inverter is raised in MATLAB/Simulink. By considering the good dynamic performance, cost minimization and low total harmonic distortion (THD), FSTP inverters are comparable with SSTP inverters. The dynamic performance and robustness of proposed FLC with the FSTP inverter fed IM drive is verified with PI controller. It is established that the suggested FLC is further powerful than the PI controller under speed tracking and load disturbances

Fuzzy controlled sensorless speed control of induction motor with MRAS based approach is proposed in this paper. Fuzzy logic controller is expected to be better controller as compared to traditional PI and PID controllers. The mathematical modeling of the MRAS module with inputs from induction motor stator voltages and currents taken as feedback to estimate the speed of the machine is described. The speed control of the induction motor is achieved by IRFOC without speed sensor at the motor. Induction motor speed and electromagnetic torque comparison with PI and FIS controllers integrated into the structure are represented by graphs plotted with time as a reference. Fuzzy controller is modeled with 49 rule base including two inputs and one output variable. All the modules design and the analysis is carried out in MATLAB software with ‘powergui’ toolbox for generating graphs.

In general, the induction motor (IM) is extremely nonlinear in nature and frequency dependent. In most cases, the power generated by the IM has a low power factor (PF), which exhibits detrimental effect on the extent to which the whole transmission and distribution system functions. Since there exists more current harmonics as an outcome of minimized PF, the efficiency of the power system suffers due to transmission line heating and voltage distortion characteristics. Therefore, this paper proposes a power factor correction (PFC) method to overcome the aforementioned issues. Here, by the utilization of AC-DC bridgeless SEPIC-Cuk converter, the power quality is improved by reducing reactive power consumption and enabling better control of voltage and current outputs. To maintain the stable DC link voltage with reduced ripples, the adaptive proportional-integral (PI) controller is used in this work. The three-phase voltage source inverter (VSI) transitioning function is controlled by cascaded fuzzy logic (CFL) controller, which is also utilized for regulating the speed of the three-phase IM. Implementing the proposed control strategy improves power quality significantly by reducing total harmonic distortion (THD). The proposed system is simulated in the MATLAB platform and the attained outcomes, it is clear that the proposed system is highly effective.

Because of its low maintenance and robustness, induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy logic controller. Induction motor is modeled in synchronous reference frame in terms of dq form. The speed control of induction motor is the main issue achieves maximum torque and efficiency. Two speed control techniques, Scalar Control and Indirect Field Oriented Control are used to compare the performance of the control system with fuzzy logic controller. Indirect field oriented control technique with fuzzy logic controller provides better speed control of induction motor especially with high dynamic disturbances. The model is carried out by using Matlab/Simulink computer package. The simulation results show the superiority of the fuzzy logic controller in controlling three-phase induction motor with indirect field oriented control technique.

Voltage-source multilevel inverters have become very attractive for power industries in power electronics applications during last years. The main purposes that have led to the development of the studies about multilevel inverters are the generation of output voltage signals with low harmonic distortion; the reduction of switching frequency. A serious constraint in a multilevel inverter is the capacitor voltage-balancing problem. The unbalance of different DC voltage sources of five-level neutral point clamping (NPC) voltage source inverter (VSI) constitutes the major limitation for the use of this new power converter. In order to stabilize these DC voltages, we propose in this paper to study the cascade constituted by three phases five-level PWM rectifier, a clamping bridge and five-level NPC (VSI). In the first part, we present a topology of five-level NPC VSI, then they propose a model of this converter and an optimal PWM strategy to control it using four bipolar carriers. Then in the second part, we study a five-level PWM rectifier, which is controlled by a multiband hysteresis strategy. In the last part of this paper, the authors study shows particularly the problem of the stability of the multi DC voltages of the inverter and its consequence on the performances of the induction motors (IM). Then, we propose a solution to the problem by employed closed loop regulation using PI regulator type fuzzy logic controller (FLC). The results obtained with this solution confirm the good performances of the proposed solution, and promise to use the inverter in high voltage and great power applications as electrical traction.

This paper describes the simulation of a control scheme using the principle of field orientation for the control of a voltage source inverter-fed induction motor. The control principle is explained, followed by an algorithm to simulate various components of the system in the digital computer. The dynamic response of the system for the load disturbance and set-point variations have been studied. Also, the results of the simulation showing the behavior of field coordinates for such disturbances are given.

This paper deals with a single stage solar powered speed sensorless vector controlled induction motor drive for water pumping system which is superior to conventional motor drive in terms of reliability and cost. The speed is estimated through estimated stator flux. The proposed system includes solar PV (photovoltaic) array, a three-phase voltage source inverter (VSI) and a motor-pump assembly. An incremental and conductance (I&C) MPPT (maximum power point tracking) algorithm is used to harness maximum power from SPV array. The smooth starting of the motor is attained by vector control of an induction motor. The desired configuration is designed and simulated in MATLAB/ Simulink platform and the design, modeling and control of the system are validated through demonstration of simulation results.

The induction motor drive is simple in construction, rugged, safe, low-maintenance, and are variable speed motor drive but the cost has increased while making variable speed drive with use of modern control schemes. Therefore, researchers are discovering ways to reduce the cost while achieving variable speed control. One way is by getting rid of speed sensor in the motor shaft. It can reduce program complexity and improve reliability while minimizing the cost. The recent trend in the sensorless control of induction motor drive is to achieve successful reference tracking of estimated speed at low or zero speed. The vector control due to decoupled control of stator flux and rotor torque in induction motor is widely applied for speed control. In this paper, a sensorless vector control of induction motor with fuzzy-based rotor-flux MRAS topology is proposed. The fuzzy logic controller in the speed estimator block has achieved tremendous improvement in the speed estimation at low and even zero speed proving it to be superior to other sensorless control methods. The controller parameter calculation methods are also presented. The model is simulated in MATLAB/SIMULINK software. The results showed satisfactory improvement in the system performance especially at low or even zero speed.

Multilevel inverters are gaining popularity in high power applications. This paper proposes a new ladder type structure of cascaded three-phase multilevel inverter with reduced number of power semiconductor devices which is used to drive the induction motor. The ultimate aim of the paper is to produce multiple output levels with minimum number of semiconductor devices. This paper uses only 11 switches along with 3 diodes and 4 asymmetrical sources to produce an output voltage of 21 levels. The modulation technique plays a major role in commutation of the switches. Here we implement the multicarrier level shifting pulse width modulation technique to produce the commutation signals for the inverter. The proposed multilevel inverter is used to drive the three-phase induction motor. The mathematical modeling of three-phase induction motor is done using Simulink. Furthermore the PI and fuzzy logic controllers are also used to produce the reference waveform of the level shifting technique which in turn produces the commutation signals of the proposed multilevel converter. The controllers are used to control the speed of the induction motor. The effectiveness of the proposed system is proved with the help of simulation. The simulation is performed in MATLAB/Simulink. From the simulation results, it shows that the proposed multilevel inverter works properly to generate the multilevel output waveform with minimum number of semiconductor devices. The PI and fuzzy logic controller performances are evaluated using the results which indicate that with the help of controllers the harmonics has been reduced and the speed control of induction motor is achieved under different loading conditions.

Induction motors are one of the most common motors in use today because of their high robustness and low maintenance. To achieve high efficiency and maximum toque, controlling the speed of the induction motor becomes essential. This paper compares the performance of Proportional-Integral (PI) Controller, Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the speed and torque of a three-phase induction motor model. The effectiveness of each method is analyzed and presented with the help of simulation results. By comparing the effect in transient and state parameters, ANFIS shows higher efficiency and robustness than FLC and PI Controller for speed control.

This paper presents the design of a novel fuzzy logic based automatic caution order system for Indian Railways. The Artificial Intelligent Fuzzy Logic Controller (FLC) is used. Simulation is done in Matlab-Simulink to control speed of the train as per the caution order. A `caution data processor' subsystem is introduced to switch from manual to fuzzy control of the traction motor at caution regions of the track & sets the speed limitation as per caution order. FLC gets activated at Caution point by the Caution data Processor and gets deactivated at the end of caution thereby passing the control of the train to its loco pilot. This system of imposing speed limitations on route sections is made automatic by the introduction of Caution Data Processor and FLC; improving on conventional speed control aspects of the train manually by the loco pilot. FLC Inputs are Speed error and acceleration. An emergency switch is also included to override the fuzzy control in the caution region, if any emergency occurs. Simulation studies were done to check the working of FLC design & the speed control of traction motor is achieved as per Caution Order. Thus FLC & Caution Data Processor provides intelligent & effective speed control of traction motor.

In this paper, a novel multi-objective optimization method based on a Genetic-Fuzzy Algorithm (GFA) is proposed. GFA is applied to optimize the five PI controller gains in the Indirect Field Oriented Control (IFOC) of an induction motor drive. The PI controller gains are designed to optimize the step response of the system. Rise-time, maximum over-shoot, settling time and steady state error are the objective functions. In this drive system, the simultaneous estimation of the rotor speed and time constant for a voltage source inverter-fed induction motor is discussed. The theory is based on the parallel Model Reference Adaptive Control System (MRAC). The vector control of the induction motor may be achieved in the rotor-flux-oriented frame. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance is also designed. The simulation results of the new method for induction motor speed control is compared with the results obtained by the conventional method, which allows better performance.

In the parallel hybrid electric vehicle (PHEV) the load torque and speed control are the key point for efficient driving vehicle. A dynamic model of PHEV derived by an induction motor (IM) and an internal combustion engine (ICE) is made using Simulink package in Matlab. A comparison between a conventional PID controller and a fuzzy logic control (FLC) method. Also, analysis of IM with FL at variable load torque and speed is given. Simulation results show that the proposed FLC of speed provides high-performance dynamic characteristics and is robust with regard to motor parameter variations and load change. Finally a model of PHEV powered with IM and ICE is shown to be valid for different road cycles.

<p>This paper directed the speed-sensorless vector control of induction motor drive with PI and fuzzy controllers. Natural observer with fourth order state space model is employed to estimate the speed and rotor fluxes of the induction motor. The formation of the natural observer is similar to and as well as its attribute is identical to the induction motor. Load torque adaptation is provided to estimate the torque and rotor speed is estimated from the load torque, rotor fluxes and stator currents. There is no direct feedback in natural observer and also observer gain matrix is absent. Both the induction motor and the observer are characterized by state space model. Simple fuzzy logic controller and conventional PI controllers are used to control the speed of the induction motor in closed loop. MATLAB simulations are made with PI and fuzzy controllers and the performance of fuzzy controller is better than PI controller in view of torque ripples. The simulation results are obtained for various running conditions to exhibit the suitability of this method for sensorless vector control. Experimental results are provided for natual observer based sensorless vector control with conventional PI controller.</p>

Conventional PI controller has overshoot effect at the transient period of the speed response curve. Most of the controllers of Induction Motor (IM) for industrial applications have been designed based on PI controller without consideration of core loss (CL) and stray load loss (SLL). In order to obtain the precise performance of torque as well as speed and flux, the mentioned losses should be considered for designing the control system. Conventionally, the gain of PI controllers is chosen by trial and error method which needs a lot of computational time. Fuzzy Logic Controller (FLC) is suitable for nonlinear system such as IM. In this paper, a FLC based speed controller is designed to control the speed of the IM taking CL and SLL into account. Simulations are carried out by using MATLAB/SIMULINK. The simulations result shows that the controller is capable to control the speed of IM without any overshoot and steady-state error effect.