Batalgorithmoptimizedfuzzy proportional derivative basedspeedcontroller for Brushless DC motor has been presented. The overall control system has been created and simulated using MATLAB/Simulink and Sim power system tools to conﬁrm the va- lidity and development of the proposed system. Effectiveness of the proposed controller is analyzed and compared with PSO, Cuckoo search algorithmoptimizedfuzzyPD, fuzzy PID controller and batalgorithmoptimizedfuzzy PID controller. In order to test the ef- fectiveness of the proposed controllers under realistic operating environment, various operating conditions such as constant load, varying load and varying set speed conditions are considered, and the performances are observed. In order to make a reasonable com- parison, several performance measures are used such as rise time, settling time, recovery time maximum overshoot, steady state error, root mean square error, integral absolute error, integral of squared error, and integral time multiplied absolute error. The results ob- tained from the simulations clearly show the drastic improvements on performance measures and proved that the disturbances are also compensated much more effectively with the use of the proposed controller. To validate the performance of the proposed controller under real time operating conditions, the experimental realiza- tion for the control of Brushless DC motor has been fabricated and tested. From the results of the simulation and experimental set up, it is made clear that the proposed controller is able to eliminate the uncertainty problem occurring due to load variations and set speed variations. Since the controller exhibits unmatched performance, it is ideal for application in process industries.
The controller can specifically control a process to the requirement by tuning the 3 components in the PID algorithm. The controller’s response can be in the form of the degree of setpoint overshooting by the controller, the responsiveness of the controller to an error, and the rate of system oscillation. It is worthy to note that using the PID controller does not ensure an optimal system stability. Some systems may need the use of 1 or 2 modes for an efficient control. This is achievable through setting the gain of undesired outputs to zero. Without the respective control actions, a PID controller can be referred to as either a PI, PD, P or I controller. The PI controllers are common due to the sensitivity of the derivative action to measurement noise; while the system may be prevented from attaining the target by the absence of an integral value as a result of control action. Further details on the PID control system is provided by .
This paper presents an anti-windup self-tuning euzzy proportional-integral-derivative (PID) controller for the speed control of brushlessdirect-current (BLDC) motor. The proposed controller consists of a self-tuning fuzzy PID controller and an snti-windup controller, which combines the merits of these two controllers. The anti-windup controller is designed as a fuzzy proportion controller and is switched by a logic function based on the output of proposed controller and the speed error. A detailed simulation study in MATLAB/Simulink is performed to investigate the performance of proposed controller, and the simulation experiments are conducted on different conditions. The control system parameters of the proposed controller such as overshoot, settling time and rising time are compared with that of the conventional PID controller. The simulation results show that the proposed controller can accelerate the response speed of BLDC motor, strengthen the ability of anti-windup and make motor runs smoothly, it is proven that anti-windup self-tuning fuzzy PID controller has better performance than conventional PID controller.
Increasing the methods of order calculus for Fractional Order Proportional Integral Derivative (FOPID) controller leads to a wide applications for this type of controller in control systems. A closed loop speed control for BrushLessDirectCurrent (BLDC) motor with FOPID controller runs the motor very close to the reference speed, provides a good performance and robustness compared with a corresponding system using conventional PID controller. In this paper, the BLDC motor is modeled and simulated in Matlab/Simulink for speed control strategy. A DC link speed control strategy is proposed and implemented. The FOPID speedcontroller parameters (Kp, ki, kd, λ ) are optimized by genetic evolutionary programming module. The effectiveness of the proposed speed control is verified through simulation. The control system is extra tested under a conditions of some motor parameters perturbations. The simulation results reveal a flexible and stable control strategy.
Abstract: Direct Torque Control of the sensor less permanent magnet brushless DC motor with fuzzy logic speedcontroller is presented in this paper. The direct torque control is one of the vector based control in variable frequency drive control. This project proposes the control of torque directly and stator flux amplitude indirectly by using direct axis current. The torque error, flux error and estimated position of the stator flux linkage vector are used to change the switching sequence of the inverter through space vector pulse width modulation to control the motorspeed. An impedance source network is coupled between the inverter and the DC supply to reduce the ripples in torque by boosting the inverter’s input voltage. The three phase voltage and current are converted to d- q axis to calculate the stator flux linkage vector, rotor angle and actual torque using park transformation. A fuzzy logic controller is used to generate a reference torque value by processing the speed errors. A prototype model is made for 24 V BLDC motor and the closed loop operation is obtained using fuzzy logic controller.
ABSTRACT: BrushlessDirectCurrent (BLDC) motors are one of the motor types rapidly gaining popularity. BLDC motors are widely used in industries such as Appliances, Automotive, Aerospace, Consumer, Medical, Industrial Automation Equipment and Instrumentation. BLDC motors are receiving wide attention due to their high efficiency, high dynamic response, better speed versus torque characteristics and small size. Conventional controllers are widely used to control BLDC motors. But they often fail to control the BLDC as they suffer from uncertain parameters and the non-linearity of the BLDC motors. Fuzzy control can be used to control the speed of the BLDC motors. In this paper, a fuzzy logic controller whose parameters are tuned on line is proposed to control the speed of a BLDC motor and is compared with conventional controllers like PI and PID.
Position sensors can be totally wiped out, along these lines diminishing further cost and size of motor gathering, in those applications in which just variable speed control (i.e., no positioning) is obliged and framework flow is not especially requesting (i.e., gradually or, at any rate, typically differing burden). Truth be told, some control techniques, for example, back-EMF and current detecting, give, as a rule, enough data to assess with adequate exactness the rotor position and, thusly, to work the motor with synchronous stage streams. A PM brushless drive that does not oblige position sensors but rather just electrical estimations is known as a sensorless drive . The BLDC motor gives an alluring contender to sensorless operation on the grounds that the way of its excitation intrinsically offers a minimal effort approach to concentrate rotor position data from motor-terminal voltages. In the excitation of a three-stage BLDC motor, with the exception of the stage compensation periods, just two of the three stage windings are leading at once and the no directing stage conveys the back-EMF. There are numerous classifications of sensorless control methods ; nonetheless, the most famous class is taking into account back electromotive powers or back-EMFs . Detecting back-EMF of unused stage is the most cost effective technique to get the substitution succession in star injury motors. Since back-EMF is zero at stop and corresponding to speed, the deliberate terminal voltage that has extensive sign to- commotion proportion can't identify zero intersection at low speeds. That is the motivation behind why in all back- EMF-based sensorless strategies the low-speed execution is restricted, and an open-circle beginning system is obliged .
Brushless DC motor (BLDC) has been widely used for many years in many industrial application because of its efficiency and reliability. There has been a massive research interest in the area of modelling and control of BLDC as reported in the literatures. Recent research, for instance, an efficient ANFIS basedspeedcontroller for BLDC motor is proposed as in . ANFIS controller is modelled through modified data of the fuzzy tuned PID controller and is simulated under varying speed and load conditions. Control system responses shows that the proposed controller is superior to the classical PI and fuzzy tuned PID controller and fuzzy variable structure controller. Batalgorithmoptimized online ANFIS for the same plant is also proposed . Their results show that the proposed controller eliminates the uncertainty problem due to load disturbance and enhance the time domain specifications and performances in all operating conditions. The same algorithm is then enhanced using the same system by proposing a fuzzy online gain tuned windup PID and fuzzy PID supervised ANFIS . The results show that the windup problem can be eliminated and also improve control system performances.
The average value of voltage (and current) fed to the load is controlled by turning the switch between supply and load on and off at a fast pace. The longer the switch is on compared to the off periods, the higher the power supplied to the load is. The PWM switching frequency has to be much faster than what would affect the load, which is to say the device that uses the power. Typically switching have to be done several times a minute in an electric stove, 120 Hz in a lamp dimmer, from few kilohertz (kHz) to tens of kHz for a motor drive and well into the tens or hundreds of kHz in audio amplifiers and computer power supplies.
The future work of this is to control the speed of BLDC motor using intelligent fuzzy logic controller. Thus the control using FLC will be more effective than this PI controller. The Future work is to implement this simulation in hardware. They have the advantage to be robust and relatively simple to design as they do not require the knowledge of the exact model. It has simple features like fixed and uniform input and output scaling factors, flat, single partition rule-base with fixed and non-interactive rules, fixed membership functions, limited number of rules, which increase exponentially with number with the number of inputs, fixed knowledge, low-Level control and no hierarchical rule structure.
In DTC, the optimum voltage space vector for the entire switching period controls the torque and flux independently and the hysteresis band maintains the errors. Only one vector is applied for the entire sampling period, in the conventional method. So, for small errors, the upper or lower torque limit may be exceeded by the motor torque. Instead, the torque ripple can be reduced by using more than one vector within the sampling period. The insertion of zero vector precisely controls the slip frequency . For a smaller hysteresis band, the frequency of operation of the PWM inverter could be very high. The width of the hysteresis band causes variation in the switching frequency. Direct torque control based on space vector modulation preserve DTC transient merits, furthermore, produce better quality steady state performance in a wide speed range. At each cycle period, SVM technique is used to obtain the reference voltage space vector to exactly compensate the flux and torque errors. The torque ripple of DTC-SVM in low speed can be significantly improved.
The speed control of the BLDC motor is studied and simulated in MATLAB/Simulink.The speed control of a BLDC Motor is presented in this paper, using both PI controller, and Fuzzy Logic Controller.The inference which can be concluded after comparison is that speed control of BLDC using Fuzzy Logic Controller has better performance than PI,PID Controllers. To add current control function to the proposed speedcontroller in order to keep the current within a certain range for a specific speed, could be a work for future. The proposed future work would thereby enhance the motor start-up current, reduce the motorcurrent ripples and overall enhance the motor torque characteristics performance. Current control methodology will also reduce the speed and torque variations caused due to any sudden changes in the motorcurrent value .
This paper presents the simulation of the control of doubly star induction motor using Direct Torque Control (DTC) based on Proportional and Integral controller (PI) and Fuzzy Logic Controller (FLC). In addition, the work describes a model of doubly star induction motor in α-β reference frame theory and its computer simulation in MATLAB/SIMULINK®.The structure of the DTC has several advantages such as the short sampling time required by the TC schemes makes them suited to a very fast flux and torque controlled drives as well as the simplicity of the control algorithm.the general- purpose induction drives in very wide range using DTC because it is the excellent solution. The performances of the DTC with a PI controller and FLC are tested under differents speeds command values and load torque.
The speed control is one of important component in DirectCurrentmotor (DC motor) operation. In controlling the speed of a DC motor, some improvement needs to be done toward speed regulation during transient loading conditions. The control includes a regulating circuit that having an output for controlling the armature voltage to the motor. An input speed reference signal corresponding to the desired speed is provided to the regulating circuit. A feedback signal proportional to armature voltage is also provided to the regulating circuit to establish an error signal for operating the control to regulate the voltage to the motor and therefore the motorspeed.
Over the past decades, many schemes are introduced for the rotor speed estimation in the sensor less vector control of IMDs [4-10]. Some of the methods are (i) method of signal injection , (ii) method of state observers , (iii) method based on model configurations . Signal injection method tolerates from difficulty calculations and essential for the outer equipments for the signal injection method. According to all the schemes of MRAS [7-10] speed estimators is usual working procedure because of their good characteristics, merely contrasting the scheme of injecting signal. Back-emf , rotor-flux , reactive power  and active power  are the most desired methods of MRAS. The method of back emf which gives good execution when speeds are high, although speed estimation is more complex at initial condition and it possess less speedy and hardly empathetic to motor constant. Hence, to prevent these disadvantages, rotor flux of MRAS method is most popular and it was first proposed by C Schnauzer in .
Abstract: Ease control and high power density are the two main reasons for using brushless DC motor in wide range of industrial applications. In general the brushless DC motors are controlled using power Semiconductor bridge. So the rotor position sensors are used in the inverter bridge for starting and to provide proper commutation sequence to the power switches. In building inverter bridge power switches are used due to high reliability and performance. In this paper the three phase brushless DC motor model is designed with proportional integral controller and tested in MATLAB software. The PI controller is used to control the speed of the brushless DC motor. On the other hand parameters like Back EMF, current, speed and torque are evaluated for the designed models of BLDC motor. Due to the easy implementation and simple control structure the convectional PI controller are used in industries.
For the tuning of the parameters of the membership functions of a fuzzycontroller a novel PSO algorithm has been developed. The algorithm for the fuzzycontroller has been encoded in MATLAB but a block diagram strategy is enabled to explain the algorithm. A SIMULINK model has been used.The Plant used is an armature controlled DC Motor. Conventional controllers like PI and PID controllers fail in case of non linearities and may generate steady state error. In such a case a fuzzycontroller is used which is basically a non-linear element whose parameters are tuned using Particle Swarm Optimization Technique (PSO) subject to the condition that steady state error is to be minimized. The quantity to be controlled is the speed of the DC Motor. Therefore error in speed is to be minimized. PSO technique is a very uncertain algorithm that may or may not converge to the optimized values. Nevertheless we got optimistic simulation results. As such it could overcome the limitations of conventional controllers.
According to Table 2, the four sub-operating modes have different rules to phase current. In mode 11, and − rise quickly, and varies proportionally with the back-EMF of phase c. In mode 12, and − drop quickly, and changes proportionally with the back-EMF of phase c. Compared with modes 11 and 12, falls much quicker in mode 13 and rises much quicker in mode 14. Thus, mode 1 could be optimized by reasonable combination of four sub-operating modes. First of all, the equation = 0 must be satisfied to keep the system stable as follows. When deviates seriously from zero, modes 13 and 14 works. When remains at zero, modes 11 and 12 works. Because and cannot be detected, a speed loop is used here to decide the duty of PWM signals. The same regulating method is used in mode 4, and asymmetric voltage vectors are eliminated.
Conventional proportional integral (PI) dynamic controls for speed and the load have drawbacks . Although fuzzy logic is commonly applied to the field oriented control of induction motors, the fuzzification, defuzzification, and decision procedures that establish a knowledge base are more complicated, difficult and time-consuming. Moreover, although some studies - have been performed in this area, most, if not all, are based on conventional trial-and-error techniques. However, optimal performance may not be achieved. The phase plane technique - is now widely used to enhance the control performance in the transient and steady states. This paper describes a new method for advancing the indirect oriented field control (IFOC) ,  technique for controlling the speed load of an induction motor. The proposed controller (FPPC) speeds up the response because the defuzzification procedure and the fuzzy rules derived from expert experiences (knowledge bases) are no longer required. The block diagram of the proposed controller design has shown in Fig. 1 for controlling speed load of an induction motor.
Abstract— The induction motor is without doubt the most used electrical motor because of its unique characteristics. Most of its applications need fast and intelligent speed control system.This paper presents an intelligent and advanced speed control based on fuzzy logic technique to achieve maximum torque and efficiency.A rule based Mamdani type fuzzy logic controller is applied to closed loop induction motor model.Scalar control(volt-hertz) method is used for controlling speed.A conventional controller is compared practically to fuzzy logic controller using Matlab/Simulink software package.The simulation results show the superiority of the fuzzy logic controller.