This paper is intended to compare the four controllers namely, P-I, I-P, Fuzzy and Neuro-Fuzzy controller for the speedcontrol 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 fuzzy logic control and neuro-fuzzycontrol to a phase controlled converter dcmotordrive. Fuzzy logic 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 speedcontrol of dcmotor drives.
The simulation of the BLDC motor is done by using MATLAB/SIMULINK technical computing software package . Its speed and torque waveforms are analyzed. A PI controller and fuzzy controller have been employed for speedcontrol of BLDC motor. Through the simulations of all controllers, it is confirmed that the proposed hybrid fuzzy PID and fuzzy logic controllers provide a good response to the successive changes in reference speed and load torque. The results obtained by simulation show the feasibility and ability of the proposed fuzzycontrollers strategy. The HFPID and Fuzzy logic controllers provide more efficient speedcontrol technique for BLDC motor with good dynamic response characteristics such as rise time, peak overshoot, steady state error and settling time. Moreover, it generates less speed and torque ripples.
The switched reluctance motor has used in high performance motorcontrol application such as aircraft starter or generator system, electric traction, mining drives, washing machines, door actuators, etc. this use is due to high reliability, high torque, robust construction, low cost of switched reluctance motor. In all applications of switched reluctance motor smooth torque is the major need but due to air gap flux harmonics increases torque ripples further increases periodic speed ripples in switched reluctance motor. Due to this, performance of drive deteriorates. The main and major objective of this paper is to analyse speedcontrol method of switched reluctance motor with pi and fuzzy controller in MATLAB/Simulink environment. Simulation will be carried out with pi and fuzzy controller for reduction of periodic speed ripples in switched reluctance motor. Fuzzy logic controller is introduced to give an effective and outstanding performance to minimize periodic speed ripples in switched reluctance motor.
Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. If the oscillations increase with time then the system is unstable, whereas if they decrease the system is stable. If the oscillations remain at a constant magnitude the system is marginally stable. A human would not do this because we are adaptive controllers, learning from the process history; however, simple PID controllers do not have the ability to learn and must be set up correctly. Selecting the correct gains for effective control is known as tuning the controller.
DCmotor is a power actuator which converts electrical energy into mechanical energy. DCmotor is used in applications where wide speed ranges are required. The greatest advantage of dc motors is speedcontrol. The term speedcontrol stands for intentional change of the drivespeed to a value required for performing the specific work process. Speedcontrol is either done manually by the operator or by means of some automatic control device. DC motors are most suitable for wide range speedcontrol and are therefore used in many adjustable speed drives . The speed torque characteristics of DC motors are much better to that of AC motors. Also DC motors provide excellent control of speed for acceleration and deceleration. DC motors have a long practice of use as adjustable speed machines and a wide range of options have evolved. In these applications, the motor should be accurately controlled to give the desired performance. The controllers of the speed that are conceived for objective to control the speed of DCmotor to execute many tasks .Speedcontrol means intentional change of the drivespeed to a value required for performing the specific work process. Speedcontrol is a different concept from speed regulation where there is natural change in speed due change in load on the shaft. Speedcontrol is either done manually by the operator or by means of some automatic control device. One of the important features of DCmotor is that its speed can be controlled with relative ease . The main advantage of DC motors is the speedcontrol facility. The term speedcontrol stands for intentional speed variation done by automatic controllers or by manual means. For further improvement of the speed response characteristics of the DCmotor, another controller called Fuzzy Logic Controller (FLC) has been developed. Fuzzy logic control is a linguistic control algorithm which uses general statements instead of the mathematical equations to define the control scheme of the responses. Due to this technique, a wide range of values are included in the set which leads to better rise time, less speed fluctuations and overshoots. With fuzzy logic controller, manual tuning is eliminated and intelligent tuning takes the centre stage with satisfactory performance. There are several conventional types such as Proportional (P), Proportional Integral (PI), Proportional derivative (PD).Proportional Integral derivative controller (PID)  and Fuzzy Logic Controller (FLC) . This paper mainly focuses on the performance evaluation of DCmotor using, Proportional Integral derivative controller (PID) and Fuzzy Logic Controller (FLC).The simulation results are presented to demonstrate the effectiveness of this controller and compared with PID controller using MATLAB / SIMULINK.
Where Kb is back EMF constant, E is back EMF per phase, and ω is the angular velocity in radians per second.The parameters of motor are phase resistance, phase induc- tance, and inertia and friction of BLDC servomotor and load. It is necessary to determine the parameters of both BLDC servomotor and load so as to design conventional controllers like P, PI, and PID controllers.The parameters that are likely to vary during the working conditions are R, JM,JL, BM, and BL. These parameters can influence the speed response of the BLDC servomotor drive sys- tem. Increase in the value of energy storage inertia ele- ments JM and JL will increase the settling time of the speed response or vice versa. The decrease in the values of power consuming friction components BM and BL will increase the deceleration time of the speed response or vice versa. Another parameter, which is likely to vary dur- ing working conditions is phase resistance of the BLDC servomotor due to addition of terminal resistance, change in resistance of phase winding, and change in on-state re- sistance of IGBT switches due to change in temperature. The change in phase resistance can also affect the speed response of the BLDC servomotor drive system. Mixed combination of inertia, friction, and phase resistance of the BLDC servomotor may lead to large overshoots that are undesirable in most of the control applications. There- fore, the BLDC servomotor drive system needs suitable controllers such as PID or Fuzzycontrollers to speed up the response, reduce overshoot, and steady-state error to meet up the applications requirements.
DC motors have a long tradition of use as adjustable speed machines and a wide range of options have evolved for this purpose due to less expensive and high power ratings. In these applications, the motor should be precisely controlled to give the desired performance. The speedcontrollers are designed for desired performance of DCmotor to execute the tasks . The various type of controllers are available worldwide i.e conventional PID, Fuzzy, FUZZY- PID etc.
A detailed Simulink model for a BLDC Motordrive system with stator current control by using Simulink blocks has been developed and operated at rated speed. Three different control schemes i.e. P (Proportional), PI (Proportional Integral) and Fuzzy Logic control schemes have been developed here. A mathematical model is easily incorporated in the MATLAB simulation and the presence of numerous tool boxes and support guides simplifies the simulation of large control system. The output waveform for speed with different controllers proves that Fuzzy Logic controllers are better than P, PI controllers. The simulation with Fuzzy Logic controller allows faster simulations with reduced time and computational resources. A speed controller has been designed successfully for closed loop operation of the BLDC Motordrive system so that the motor runs at the reference or commanded speed. The modelled simulated system has a fast response with least error thus validating the design method of the speed controller.
The driving circuitry consists of inverter, which has six switches to energize two BLDC motor phases concurrently. The rotor position determines the switching sequence of the switches, is detected by the means of three hall sensors. Using the hall sensors information as well as the sign of reference current which is produced by the reference current generator the decoder block generates signal of back emf.
The objective is to implement FLC for controlling the speed of a dcmotor. The change of speed of error plays important role to define controller input. Consequently FLC uses error (e) and change in error (ce) for linguistic variables which are generated from control rules. The output variable is the change in control variable of the motordrive. To overcome the problem of PID parameter variation, a normalized fuzzy controller with adjustable scale factor is suggested. In this paper the fuzzy controller is built according to accumulative knowledge of the previous tuning methods. The fuzzy controller designed has the following parameters.
by using Proportional Integral(P-I), Integral Proportional(I- P), Proportional Integral derivative(PID) controllers and fuzzy logic controller(FLC). Ziegler-Nichols method is used to design P-I, I-P and PID controllers. Fuzzy logic controller is designed by 49 fuzzy rules set with two inputs speed error and change in speed error and one output. These controllers are developed with the help of MATLAB/SIMULINK.
Abstract: DC servo motor is widely used in many applications like Robotics, Conveyor Belts and Camera. In this paper a dc servo motor using MATLAB has been designed whose speed may be investigated using conventional controllers and FUZZY, SMC, ANFIS controllers are applied to control the speed of servomotor, that gives better response, when compared to conventional controllers. In this paper a comparison among fuzzy logic controller, sliding model controller, Adaptive Neuro-Fuzzy Inference System (ANFIS) through MATLAB/Simulink software have been presented.
Fuzzy logic control is based on the Fuzzy set theory. In fuzzy set theory, each element has a degree of membership with which it belongs to any particular set. We can say that fuzzy sets are like classical sets without much sharper boundaries. Fuzzy Logic Controller (FLC) is more used when the precision required is moderate and the plant is to be devoid of complex mathematical analysis. Other advantages are:
In order to attain the reduction of switching loss, low electromagnetic Interference (EMI) noise, and high power density effectively, the introduction of soft- switching technologies is useful in the HF resonant (HF- R) inverter. The soft-switching HF-R inverter which has been developed so far has attractive features such as the low cost and simple control schemes based on pulse frequency modulation (PFM) and pulse width modulation (PWM). However, the HF-R inverters controlled by PFM have the inherent technical issue, i.e., switching frequency limitation for the low–medium output power settings; thereby, the wide-range power regulation of the BLDC load cannot be ensured. The Resonant inverter suitable for the coupled working coils is proposed, but performances on the soft
They are applied to important fields such as variable speed drives, control systems, signal processing, and sys- tem modeling. Artificial Intelligent systems, means those systems that are capable of imitating the human reasoning process as well as handling quantitative and qualitative knowledge. It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time-varying environment, are important in tackling practical computing problems. ANFIS has gain a lot of interest over the last few years as a powerful technique to solve many real world problems. Compared to conven- tional techniques, they own the capability of solving prob- lems that do not have algorithmic solution. Neural net- works and fuzzy logic technique are quite different, and yet with unique capabilities useful in information process- ing by specifying mathematical relationships among nu- merous variables in a complex system, performing map- pings with degree of imprecision, control of nonlinear system to a degree not possible with conventional linear systems [5-11]. To overcome the drawbacks of Neural networks and fuzzy logic, Adaptive Neuro-Fuzzy Infer- ence System (ANFIS) was proposed in this paper. The ANFIS is, from the topology point of view, an implemen- tation of a representative fuzzy inference system using a Back Propagation neural network structure.
The open loop control strategy is the simplest way to control the different parameters of a drive system. In this method the inverter input is not directly connected and bounded with output. The technique is initially set as per the desired output if output changes there will be requirement of some external measurement for change in input supply. Fig shows the three phase inverter circuit using 180 deg mode of conduction. This simulation is performed on Simulink. IGBT is used as switching device for the inverter. Pulse generator is used for gating signal for switching device. Then DC is converted in to AC which is fed to three phase resistive load. Then scope is used to measure all the voltage and current.
According to zero-order Sugeno method and all the variable membership function, a neuro-fuzzy controller have 72 rules, where these rules used to give appropriate output. The output of the ANFIS are crisp value varies from V0 to V7, to generate three digital logic signals, that select the proper switching states of the inverter by using logical operation depending on Table 2. The switching state is the input to the inverter, where ‘1’ represents the upper limb switches and ‘0’ represents the lower limb switches of the inverter. Switching states of the in- verter varies from V0 to V7.
This paper presents a methodology for rule base fuzzy logic controller (FLC) applying to the system under control. Before starting the simulation in MATLAB/SIMULINK, the FLC is to be constructed, but this may involve the use of various steps, first fuzzy inference system (FIS) Editor is opened and this file is being created using the fuzzy logic toolbox. The construction of a FLC requires the selection of proper membership functions. After the appropriate membership functions are chosen rule base is generated. The set of linguistic rules is the key part of a fuzzy controller. Different linguistic variables used in the design of a rule base for output of the FLC are enlisted in Table 1; the response of the FLC is being acquired by using in SIMULINK/MATLAB. Normally two inputs, one is the speed error having the 5 membership functions, two trapezoidal and three triangular and the other is the change in error having 3 membership functions, two trapezoidal, one triangular. Also have one output change in control having 5 membership functions and all are triangular in shape. When the membership functions and Fuzzy rules are resolute, the Surface viewer is developed showing the correlation among the inputs and outputs, thus a properly controlled control output signal is obtained with the use of FLC. Membership function for inputs is as shown in Fig 6(a) and 6(b) respectively while the membership function for output is shown in Fig 6(c) Mamdani type of inference system with Centroid method for fuzzification are being implemented, 7 rules are being used in this methodology, below figure shows a basic FIS Editor.
Genetic operators such as crossover and mutation are ap- plied to the parents in order to produce a new generation of candidate solutions. As a result of this evolutionary cycle of selection, crossover and mutation, more and more suitable solutions to the optimization problem emerge with- in the population. Increasingly, GA is used to facilitate FLSs design . However, most of the works discuss type-1 FLC design. This paper focuses on genetic algo- rithm of type-2 FLCs. There are two very different ap- proaches for selecting the parameters of a type-2 FLS . Type-2 FLCs designed via the partially dependent ap- proach are able to outperform the corresponding type-1 FLCs , The type-2 FLC has a larger number of de- grees of freedom because the fuzzy set is more complex. The additional mathematical dimension provided by the type-2 fuzzy set enables a type-2 FLS to produce more complex input-output map without the need to increase the resolution. To address this issue, a comparativestudy involving type-2 and type-1 FLCs with similar number of degrees of freedom is performed. The totally independent approach is adopted so that the type-2 FLC evolved using GA has maximum design flexibility.
A brushless dcmotor is defined as a permanent synchronous machine with rotor position feedback. The brushless motors are generally controlled using a three phase power semiconductor bridge. The motor requires a rotor position sensor for starting and for providing proper commutation sequence to turn on the power devices in the inverter bridge. Based on the rotor position, the power devices are commutated sequentially every 60 degrees. This eliminates the problems associated with the brush and the commutator arrangement, for example, sparking and wearing out of the commutator brush arrangement, thereby, making a BLDC more rugged as compared to a dc motor.As illustrated in figure 1 in a BLDC motor permanent magnets are mounted on the rotor, with armature windings being fixed on the stator with a laminated steel core.