Abstract—This paper presents the positioncontrol of DCmotor by using a fuzzylogiccontroller (FLC) with MATLAB application. Firstly, the DCmotor was modeled and converted to a subsystem in Simulink. Then the fuzzylogiccontroller is designed by using MATLAB toolbox. The scopes include the simulation and modeling of DCmotor, fuzzycontroller and different transfer function for DCmotor model as a benchmark to the performance of the fuzzy system. The positioncontrol is an adaptation of assisted lifting device system. Fuzzylogiccontrol can play an important role because knowledge-based design rules can be easily implemented in the system with unknown structure and it is going to be popular since the control design strategy is simple and practical. This makes FLC an alternative method used in a nonlinear industrial system. The results obtained from FLC are compared with different transfer function used for the dynamic response of the closed-loop system and also the system with and without a controller will be compared. Parameters such as peak position in degree, settling time in second and maximum overshoot in percent will be part of the simulation result. The overall performance shows that system with FLC performs better than compared to the system without FLC.
Diode Dfp is connected in parallel to the primary winding of the transformer and diode Dr is serially connected with secondary winding across the dc link. There is one snubber Capacitor connected in parallel to each switch of phase leg. The snubber capacitor resonates with the primary winding of the transformer. The emitters of the three auxiliary Switches are connected together. Thus, the gate drive of these auxiliary switches can use one common output dc Power supply in an entire PWM cycle, all the six main switches can be turned off in the ZVS condition as the snubber capacitors (Cra, Crb, Crc) can slow down the voltage rise rate. This enables the turn-off power losses be reduced and the turn-off voltage spike eliminated. Before turning on the main switches, the corresponding auxiliary switch (Sa, Sb, Sc) must be turned on ahead. Main switches get the ZVS condition
The “Real-Time FuzzyLogicPosition Controlled DCMotor Drives” project is needed interface software and hardware. This project is build to control the performance of dcmotor for positioning purpose and controlled by fuzzylogiccontroller. Beside that, this project is build to comprehend the basic concepts of fuzzylogic. In this chapter will discuss about general background, concept of project, objective, scope, problem statement and report outline.
PID still very preferred in industry and at many automatic control applications, and it also used with BLDC motor, but the problem which rises with PID technique are the non-linear systems, the problem of affecting the speed after adding any additional loads, suffering from changing dynamics after a long time operation which will be very difficult to be covered with a fixed PID controller. Moreover, the external noise which make PID controller not be the perfect choice to control the BLDC motor in these circumstances because it means that we need to change the parameters dynamically. All of these reasons leading us to think about a more flexible and suitable controller like fuzzylogiccontroller which depends on the linguistic rules that make the system more flexible and more dynamic. And if we need to talk about a more dynamic controller shifting us from a flexible controller to a new zone of an intelligent controller we will talk about using ANFIS (Adaptive Neuro Fuzzy Inference Systems) controller which makes our system dynamic because of using training , learning and testing techniques which make an enhancement in the performance .
ABSTRACT: This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzylogic. Such a mixed implementation leads to a more effective control design with enhanced system performance and robustness. While conventional control allows diverse design objectives such as steady state and transient characteristics of the closed loop system to be precise, fuzzylogic are to overcome the problems with uncertainties in the plant constraints and structure encountered in the classical model-based propose. Induction motors are characterised by multifarious, highly non-linear and time-varying dynamics and isolation of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motorcontrol problems; because they are sensitive to drive parameter variations and performance can deteriorate if conventional controllers are used. Fuzzylogic controllers are considered as potential applicant for such an application. Two control approaches are developed and compared to adjust the speed of the drive system. The first control design is only the vector control. And second one is a fuzzy state feedback controller is developed. A simulation study of these methods is presented and results are compared. The effectiveness of this controller is demonstrated for different operating conditions of the drive system.
Most of the fuzzylogic applications with the physical systems require a real-time operation to interface high speed constraints. The simple and usual way to implement these systems is to realize it as a software program on general purpose computers, these ways cannot be considered as a suitable design solution. Higher density programmable logic device such as Field Programmable Gate Arrays (FPGAs) can be used to integrate large amounts of logic in a single IC. FPGAs are one of the fastest growing parts of the digital integrated circuit market in recent times. Dynamically reconfigurable FPGA systems can adapt to various computational tasks through hardware reuse. FPGA becomes one of the most successful of technologies for developing the systems which require a real time operation. FPGAs are more sufficient than the simple way because they can cover a much wider range of operating conditions. Semi-custom and full-custom Application Specific Integrated Circuit (ASIC) devices are also used for this purpose but FPGA provide additional flexibility: they can be used with tighter time-to-market schedules. FPGA places fixed logic cells on the wafer, and the FPGA designer constructs more complex functions from these cells .In control applications, in order to get better control responses, ,  Controller is implemented in FPGA.FPGA are two dimensional arrays of logic blocks and flip-flops with an electrically programmable interconnection between logic blocks. The interconnections consist of electrically programmable switches which is why FPGA differs from Custom ICs, as Custom IC is programmed using integrated circuit fabrication technology to form metal interconnections between logic blocks. FPGA provides its user a way to configure: The intersection between the logic blocks and the function of each logic block. Logic block of an FPGA can be configured in such a way that it can provide functionality as simple as that of transistor or as complex as that of a microprocessor. It can used to implement different combinations of combinational and sequential logic functions.
The simulated results show that, using a type-2 FLC in real world applications can be a good option since this type of system is a more suitable system to manage high levels of uncertainty, as we can see in the results shown in Tables 2-5. Simulation results indicate that the per- formance of the GA FLCT2 will better. That is mean the system will sense for change the value of IAE and ITAE. The results demonstrate that a type-2 FLC can outper- form type-1 FLCs that have more robustness design pa- rameters. The main advantage of the type-2 FLC appears to be its ability to eliminate persistent oscillations, espe- cially when unmodelled dynamics were introduced. This ability to handle modeling error is particularly useful when FLCs are tuned offline using GA and a model as the impact of unmodelled dynamics is reduced. The sig- nificance of the work is focused to manage the uncer- tainty of the system. The nonlinear of the systems are big problem therefore the one of successful methods to eli- minate or reduce nonlinearity system by using fuzzy type two. It is a good option for real time applications that limited time is needed such as robot system.
The pioneering work on fuzzylogic was done by Professor Lofti A. Zadeh of the University of California at Berkeley in 1965. He proposed the fuzzy set theory which led to a new control method called ‘fuzzycontrol’. The work of Zadeh began to gain recognition after Dr. E.H. Mamdani of London University practically applied the concept of fuzzylogic to the automatic control of steam engine in 1974. During the past few decades, FuzzyLogic has been widely applied in almost all aspects of our society including industrial manufacturing, automatic control, automobile production, banks, hospital, etc. Today, fuzzylogic is still well recognized. Worldwide, thousands of studies are carried out yearly on this field , .
This paper contains, performance of fuzzy controllers which evaluated and compared. It also describes the speed control based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of DCmotor, which merely focuses on performance of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The controller is designed using MATLAB software, the results shows that the fuzzy controllers are the good but it as higher overshoot in comparison with optimal LQR. Thus, the comparative study recommends LQR controller gives better performance than the other controllers.
In this paper, performance of the FLC and PID controller is observed for separately excited DCmotor speed control, when input function is step. A system should be designed for less rising time and less overshoot percentage. The performance comparison study between FLC and PID controller whose parameters adjusted using Simulink Tuning Tool which gives better parameters than other tuning methods is carried out. Even though, better tuned PID controller may be provide better performance, the recommended FLC has more benefits, such as better static and dynamic performance, higher flexibility, control, compared with PID controller. Furthermore, it can be seen in Figure 10-11, FLC have provided less overshoot percentage of 4.2 % and lower rising time 2.8386 second, which means FLC has better performance in terms of overshoot percentage, settling time, control performance, and rise time, therefore, FLC design was recommended and implemented. REFERENCES
Recently, BLDC motors are very popular compared with the brushed DC motors. In short, BLDC motors have some advantages over conventional brushed DC motors and induction motor. Some of these advantages are better speed versus torque characteristics, high dynamic response, high efficiency, high power density, long operating life, noiseless operations and has higher speed ranges. In addition, BLDC motors are reliable, easy to control, low maintenance and it is inexpensive , , . Due to their favourable electrical and mechanical properties, thus they are widely used in servo applications such as automotive, aerospace, medical instrumentation, robotics, machine tools, textile, industry automation equipment and many more as studied by researchers like Lee and Ehsani (2003). The small sized of motors with external rotors are widely used in visual equipment such as computer disc drives, tape recorders and digital audio tapes . BLDC motors are one of the motor types rapidly gaining popularity. Therefore, the motor must be controlled precisely so that it will give the desired performance in all these applications.
A fuzzycontrol system is a control system in which a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively). FuzzyLogic provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. Fuzzy Logic’s approach to control problems mimics how a person makes decisions, only much faster. Fuzziness is connected with the degree to which events occur rather than likelihood of their occurrence.
The main motive of constructing a PID controller is to regulate and control the speed performance of DCmotor. In PID controller, the proportional action is used for the amplification with an adjustable gain , the integral control action is called as reset control and the derivative control action is called as rate control. When all the three control actions are merged then this controller is known as PID controller. The simulink diagram of speed response with PID controller is given below :-
Now imagine a light bulb with a switch. When the switch is closed, the bulb goes on and is at full brightness, says 100 Watts and when the switch is closed is 0 Watts. Now if the switch is closed in a fraction of a second and then opens for the same amount of time, the filament would not have time to cool down and heat up. This will just get an average glow of 50 Watts. This is how lamp dimmers work, and the same principle is used by speed controllers to drive a motor. When the switch is closed, the motor sees 12 Volts, and when the switch is open it sees 0 Volts. If the switch is open for the same amount of time as it is closed, the motor will see an average of 6 Volts, and will run more slowly accordingly. As the amount of time that the voltage is on increases compared with the amount of time that it is off, the average speed of the motor increases.
Abstract: Direct Torque Control of the sensor less permanent magnet brushless DCmotor with fuzzylogic speed controller 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 motor speed. 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 fuzzylogiccontroller 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 fuzzylogiccontroller.
The project is titled as “Development of FuzzyLogic Speed Controller for DCMotor Applications by Using Rabbit Microprocessor.” In this project, the embedded controller developed is based on Rabbit microprocessor and its core module model RCM 3100. The control method implemented in this is fuzzylogiccontrol method. A fuzzylogic algorithm needs to be developed and be compiled in Rabbit microprocessor in order to provide precise control signal for DCmotor variable speed drive. Instead of using assembly language to compile the fuzzy algorithm, this controller will use Dynamic C programming language to develop the algorithm.
Figure 2.1 shows a fuzzylogiccontrol system in a closed loop control. there are four important elements in the fuzzylogiccontroller system structure which are fuzzifier, rule base, inference engine and defuzzifier. Details of the fuzzylogiccontroller system structure can be seen in in figure below. Firstly, a crisp set of input data are gathered and converted to a fuzzy set using fuzzy linguistic variables, fuzzy linguistic terms and membership functions. This step also known as fuzzification. Afterwards, an inference is made base on a set of rules. Lastly, the resulting fuzzy output is mapped to a crisp output using the membership functions, in the defuzzification step.
This paper presents Hybrid fuzzylogiccontroller (HFLC) for the well developed and sophisticated simulation model of Brush less DC (BLDC) motor drive using MATLAB. The developed simulation model has been examined by the PID controller and HFLC. The performance of the controllers is evaluated more precisely from various simulation studies for variations in the load torque and speed of BLDC motor drive. A performance comparison of two controllers is also carried out by taking various performance measures such as settling time, steady state error, peak overshoot, the integral of the absolute value of the error (IAE) and the integral of the time-weighted squared error (ITSE). The results confirm that the developed simulation model is very convenient for the precise evaluation of performance and the HFLC shows improved performance over the PID controller in terms of disturbance rejection or parameter variation.
Abstract— This paper presents a comparative analysis of speed control of brushless DCmotor (BLDC) drive fed with conventional two-level, three and five level diode clamped resonant inverter (DC- MLI). The performance of the drive system is successfully evaluated using FuzzyLogic (FL) based speed controller. The control structure of the proposed drive system is described. The speed and torque characteristic of conventional two-level inverter is compared with the three and five-level resonant inverter (MLI) for various operating conditions. The three and five level diode clamped resonant inverters are simulated using IGBT’s and the mathematical model of BLDC motor has been developed in MATLAB/SIMULINK environment. The simulation results show that the Fuzzy based speed controller eliminate torque ripples and provides fast speed response. The developed FuzzyLogic model has the ability to learn instantaneously and adapt its own controller parameters based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.
Converter operation in steady-state is best described over a period that begins from the phase α to 2п+ α. This operation involves two circuit modes during a single period of the source waveform depending upon the state of the switches as shown in Figure 2.8. Mode 1 starts when the SCRs T1 and T3 are turned on at an angle α by control pulses applied at their gate terminals. During mode 1, SCRs T1 and T3 are in forward-biased mode and SCRs T2 and T4 are in reverse blocking mode. The current Io flows through the path shown in Figure 2.11. After angle п, the input source voltage become negative but the SCRs T1 and T3 still conducting. Note that the current sink is the model of a high value inductor, voltage across it can change instantaneously but current cannot. Hence, the output voltage, Vo become negative and follows the input voltage, Vs waveform. The input source is supplying power to the load during α to п which is referred also as the rectifier operation.