Various linear and nonlinear adaptive controllers have been reported for IPMSMdrive. In , Choy et al. developed a model reference adaptive controller (MRAC) for position control of servo PMSM drive. In this method, the drive forces the response to follow the output of the reference model regardless of the drive parameter changes. The output of the system is then compared to a desired response from a reference model, and the control parameters are updated based on this error. In this case, MRAC is used in the outer loop and a PI controller is used in the inner loop. Steady state error of the PI controller is used to compensate the chattering problem due to discontinuous control inputs. However this still does not completely solve the chattering problem. Sozer and Torrey  proposed an adaptive flux weakening control of PMSM drive where the d- axis current is adjusted using direct MRAC. However, the performance of the controller was tested in limited condition. Namudri and Sen  proposed a sliding mode controller (SMC) for a self-controlled synchronous motor. The drive system employs a phase controlled chopper and GTO inverter to provide torque-producing current component. Due to frequency limitation of GTO, this method is not suitable for HPVSD. In , Consoli and Antonio proposed a DSP based vector control of IPMSMdrive using another SMC for torque control, and also tested the performance above the rated speed using flux weakening technique. The effect of constant acceleration, constant speed and constant deceleration were considered for designing SMC and variable bandwidth was used to reduce the chattering problem. But the drive was not proved in real-time.
Abstract—This paper presents a power factor corrected (PFC) bridgeless (BL) buck–boost converter-fed brushless direct current (BLDC) motor drive as a cost-effective solution for low-power applications. An approach of speed control of the BLDC motor by controlling the dc link voltage of the voltage source inverter (VSI) is used with a single voltage sensor. This facilitates the operation of VSI at fundamental frequency switching by using the electronic commutation of the BLDC motor which offers reduced switching losses. A BL configuration of the buck–boost converter is proposed which offers the elimination of the diode bridge rectifier, thus reducing the conduction losses associated with it. A PFC BL buck–boost converter is designed to operate in discontinuous inductor current mode (DICM) to provide an inherent PFC at ac mains. . In this work, conventional PI and fuzzylogiccontrollers have been used for the speed control of BLDC motor drive. The performance of conventional PI and Fuzzycontrollers are compared under variable reference speed and varying supply voltages with improved power quality at ac mains. The obtained power quality indices are within the acceptable limits of international power quality standards such as the IEC 61000-3-2. The performance of the proposed drive is simulated in MATLAB/Simulink environment
This paper is intended to compare the four controllers namely, P-I, I-P, Fuzzy and Neuro-Fuzzy controller for the speed control of a phase-controlled converter dc separately excited motor-generator system. I-P controller’s performance was compared with that of conventional P-I controlled system. It is observed that I-P controller provide important advantages over the traditional P-I controller like limiting the overshoot in speed, thus the starting current overshoot can be reduced. The paper also demonstrates the successful application of fuzzylogic control and neuro-fuzzy control to a phase controlled converter dc motor drive. Fuzzylogic was used in the design of speedcontrollers of the drive system, and the performance was compared with that of neuro-fuzzy controller. The performance of the two fuzzy-based controller are compared and it is ovserved that the performance of Neur-fuzzy controller is slightly better than that of conventional fuzzy controller. The advantages of the Neuro-Fuzzy controller are that it determines the number of rules automatically, reduces computational time, learns faster and produces lower errors than other method. By proper design a Neuro-Fuzzycontrollers can replace P-I, I-P and Fuzzycontrollers for the speed control of dc motor drives.
This paper presents two controllers for implementing the current multiplier approach over a wide range of speed control of a Brushless DC (BLDC) Motor drive system as a cost effective low-power solution. A CUK converter at the front end feeds the DC bus of the Voltage Source Inverter (VSI), where, closed loop duty ratio control of the converter results in variable DC bus voltage, enabling close matching of the reference speed setting. Two alternate controller configurations viz. PI controller and fuzzy controller are introduced for generating gate trigger signals for the power MOSFET switch of the converter. Comparison of performance covering a over a wide range of operating speed of the entire system using PI/FUZZYcontrollers is carried out by simulation in MATLAB/Simulink platform.
Fig. 6. (a) Inverter output voltage; (b) DC input current and developed torque; (c) Stator current in phases A, B and C; (d) Speed response Fig. 6. (a) Shows line voltage across phases A, B and C. The voltage is rising from zero and saturate when the motor attain the rated speed 1500 RPM. Fig. 6. (b) Shows DC input current and developed torque in a PMBLDC motor with PSO tuned PI controller. By using a tuned PI controller in a BLDC motor will limit the starting current. So the motor will start with a low starting current and appropriate starting torque. Fig. 6. (c) Shows the stator current of a closed loop BLDC motor with PI controller, having rectangular shape and less harmonics during starting and running conditions. Fig. 6. (d) Shows rotor speed in a PMBLDC motor with PSO based PI controller during steady state operation and the motor will attain the rated speed at 3 rd second.
Most of the application of control systems nowadays used the Proportional Integrated Derivative (PID) controller. Although the PID controller is simple and easy to practice, the linear PID control method is not working well in ac induction motor drive because of the nonlinearity properties of induction motor. The traditional controller such as PID controller does not give a satisfactory response when loading various conditions and different control parameters. In recent years, the artificial intelligent (AI) techniques such as fuzzylogic controller have shown high potential for induction motor application. The needs for an intelligent system controller that has the capability to control nonlinear, uncertain systems is important to improve the performance of induction motor speed controller. In fact, a new controller is need to be develop using intelligent system to guarantee the stable operation even there is a change in the parameter of the induction motor and sudden load variation.
The last three decades AC machine drives are becoming more and more popular, especially Induction Motor Drives (IMD) and Permanent Magnet Synchronous Motor (PMSM), but with some special features, the PMSM drives are ready to meet sophisticated requirements such as fast dynamic response, high power factor, and wide operating speed range like high performance applications, as a result, a gradual gain in the use of PMSM drives will surely be witness in the future market in low and mid power applications. Now in a permanent magnet synchronous machine, the dc field winding of the rotor is replaced by a permanent magnet to produce the air-gap magnetic field. Having the magnets on the rotor, some electrical losses due to field winding of the machine get reduced and the absence of the field losses improves the thermal characteristics of the PM machines hence its efficiency. Also lack of mechanical components such as brushes and slip rings makes the motor lighter, high power to weight ratio which assure a higher efficiency and reliability. With the advantages described above, permanent magnet synchronous generator is an attractive solution for wind turbine applications also. Like always, PM machines also have some disadvantages: at high temperature, the magnet gets demagnetized, difficulties to manufacture and high cost of PM material. PM electric machines are classified into two groups: PMDC machines and PMAC machines. The PMDC machines are similar with the DC commutator machines; the only difference is that the field winding is replaced by the permanent magnets while in case of PMAC the field is generated by the permanent magnets placed on the rotor and the slip rings, the brushes and the commutator does not exist in this machine type. For this reason the machine is simpler and more attractive to use instead of PMDC. PMAC can be classified depending on the type of the back electromotive force (EMF): Trapezoidal type and Sinusoidal type.
A learning environment can be considered as adaptive if it is capable of monitoring the user’s activities, interpret them following specific domain models, infer user’s requirements and preferences beyond interpreting the activities, in order to finally act on all the users’ acquired knowledge and dynamically facilitate the learning process. The adaptive behavior of environments presents diverse manifestations: adaptive interactions, the delivery of adaptive courses, articulation of instructional material and help in adaptive collaboration. The adaptive interactions are the modifications that take place in the system’s interface in order to facilitate interactions without modifying the content. For instance, in adaptive interactions we use diagrams or graphics, different text font size, or reorganization of tasks using metaphors at a semantic level. The delivery methods of adaptive courses are the most common techniques used nowadays. Particularly, this concept refers to the courses tailored to an individual student. The goal is to optimize the adaptation between the content and the user/student characteristics. Examples of these techniques are the dynamic restructuring of the course, the help in adaptive navigation, and the adaptive selection of course materials .
Abstract —Two controllers which extend the PD+I fuzzylogic controller to deal with the plant having time varying nonlinear dynamics are proposed. The adaptation ability of the first self tuning PD+I fuzzylogic controller (STPD+I_31) is achieved by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed motions while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller using same method of comparison study.
using the advance AI Technique methods. In this System the vector control scheme in the stator flux oriented reference frame is used for controlling the variable speed Induction motor. For this the conventional Speed PI controller and Current PI Controllers are tuned and the responses are observed. The Conventional Speed PI Controller is then replaced by the FuzzyLogicSpeed Controller to observe the various responses of the system. The fuzzyLogicSpeed Controller is designed and tuned in such a way to obtain better and fast sped responses of the system. Simulation results reveal that the fuzzy-controller improves the performance of variable speed Induction Motor in terms of speed and Power factor.
The SRM has a salient pole stator with concentrated windings and also a salient pole rotor with no magnets or coils. In this study 3 phases, 6/4 pole is used [1, 3]. The movement of the rotor, hence the production of torque and power, involves switching of currents into stator windings when there is a variation of reluctance; therefore, this variable speed motor drive is referred to as a switched reluctance motor drive.
There are mainly two types of dc motors used in industry. The first one is the conventional dc motor where the flux is produced by the current through the field coil of the stationary pole structure. The second type is the brushless dc motor where the permanent magnet provides the necessary air gap flux instead of the wire-wound field poles. BLDC motor is conventionally defined as a permanent magnet synchronous motor with a trapezoidal Back EMF waveform shape. As the name implies, BLDC motors do not use brushes for commutation; instead, they are electronically commutated. Recently, high performance BLDC motor drives are widely used for variable speeddrive systems of the industrial applications and electric vehicles.
Over the decade, the usage of fuzzylogic controller have been increase in the power electronic and automotive field. This is mainly because of the easy tuning features of the fuzzylogic and the simple structure of the fuzzylogic controller which ease the implementation of the controller to the machine or system. Fuzzylogic controller is easy to implement compared to other current controller such as the Proportional Integral (PI) Controller and Proportional Integral Derivative (PID) Controller. The fixed controlled parameter of fuzzylogic controller will degrade the speedperformance for different speed command. Therefore, the fixed controlled parameter need to be keep constant so that the speedperformance behaviour will be constant. However, the re-tuning and designing of membership function in FLC for difference speed command is one of the problem facing in this project since there is a lot of way to design the membership function . It is hard to decide which range and shape of the membership function should use for this project. Besides, it is also difficult in changing and tuning the fuzzylogic controller (FLC) in order to obtain good speed response since FLC have variation of rule . The designing and tuning of the fuzzylogic controller are all by trial and error method, which mean that the process are time consuming and depend on the knowledge and experience of the user to design and utilize the fuzzylogic controller.
Abstract - This paper represents that lane coloration has become popular in real time vehicular adhoc system. Lane detection is normally helpful to localize path limits. Determine undesired lane variations and to enable approximation of the upcoming geometry of the road. There are different types of methods that are used for detecting lines i.e. Hough transform, clustering and curve fitting. The paper shows that Hough transform, clustering and curve fitting work efficiently, but problem is that may fail or not give efficient results when there are curved lane road images. The objective of this paper is to improve lane coloration algorithm by modifying the Hough transform i.e. fuzzylogic with different performance metrics to improve the accuracy. Extensive experiments have shown that proposed technique outperforms over the available techniques.
This paper is concerned with vector control of permanent magnet synchronous motor (PMSM) using two different type of speed controller, one is PI controller and another is FUZZYlogic controller. Although Proportional Integral Controller usually preferred as a speed controller due to its fixed gain and integral time constant but the performance of PI controller is affected by parameter variation, such as load changing, speed variation etc. In PI controller THD of the stator phase current is more and torque ripple also more. To avoid this problem here we used FUZZYlogic controller. In this paper the mathematical model of PMSM, using the powerful simulation modeling capabilities of Matlab/Simulink is implemented. The entire PMSM control system is divided into several independent functional modules such as PMSM body module, inverter module and coordinate transformation module and Sinusoidal pulse width modulation (SPWM) production module and so on. Here we used two loops, one is outer loop known as speed control loop, another is inner loop called current loop. we can analyzed a variety of simulation waveforms and it provide an effective means for the analysis and design of the PMSM control system.
This paper introduces two common processing methods for the covariance matrix. Specifically, the fuzzy control algorithm is coupled to form the new algorithms CF and FA. In the simulation experiments, the paper explores the effects of the CF and FA algorithms on the ensemble transform matrix. Overall, the FA algorithm can be selected when the system is in weak assimilation, whereas both algorithms can be implemented in medium assimilation situations. If the system is strongly assimilated, the CF algorithm has demonstrated more robust performance.
The proposed method of MRAS which are based on PI current controller and Fuzzycurrent controller approach. MRAS methods are one of the most popular methods which are used for rotor speed estimation to detect the sensor speed at different speeds. Overall the control of MRAS speed estimation , . The stability theorems of non linearity are Lyapunov’s stability or Popov’s stability schemes of an adaptation law used the design of system is relatively stable. In Significance of identifying the vigorous response of the combining methods which do not produced by the stability theorem of nonlinearity, the adaptation law of stability additionally cannot be undertaken for lessspeed manner of regeneration. In order to conduct this type of researches, this is essential to adjust induction machine of six phase equations throughout the control point and it transfers the function of the MRAS, which is presented in this paper.
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using compensation technique. Shunt Active Power Filter (SAPF) is used to eliminate harmonic current and also it compensates reactive power. In this work, FuzzyLogicbased PI Controller based three-phase shunt active filter is employed for a three-phase four wire systems. The advantage of fuzzy control is that it provides linguistic values such as low, medium, high that are useful in case where the probability of the event to occur is needed. It does not require an accurate mathematical model of the system. A MATLAB/SIMULINK has been used to perform the simulation. Simulink model is developed for three phase four wire system under balanced source condition. The performance of balanced source is done by using FuzzyLogicbased PI controller. Simulation results are obtained and compared by applying different rules i.e. 9, 25, 49 and 81.
ABSTRACT: Due to high power density and ease of control, brushless dc motor finds its usefulness in various fields such as in battery operated vehicle, wheel chairs and in much industrial application. Three phase semiconductor bridge is used to control this motor. Speed control is achieved in permanent magnet motors usually through conventional controllers. But these conventional controllers pose difficulties when the BLDC control systems is non linear or if there is some load disturbance and parametric variations. To overcome with these problems, an artificial intelligent technique such as fuzzylogic is used. The proposed system is modelled using MATLAB/Simulink.