This paper is concerned with vector control of permanent magnet synchronous motor (PMSM) using two different type of speedcontroller, one is PIcontroller and another is FUZZYlogiccontroller. Although Proportional Integral Controller usually preferred as a speedcontroller due to its fixed gain and integral time constant but the performance of PIcontroller is affected by parameter variation, such as load changing, speed variation etc. In PIcontroller THD of the stator phase current is more and torque ripple also more. To avoid this problem here we used FUZZYlogiccontroller. 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.
Abstract: PMSM motor drives fed by dual inverter is purposely designed to reduced size and cost with respect to single motor drives fed by dual inverter. Previous researches on dual motor drives only focus on the modulation and the averaging techniques. Only a few of them, study the performance of the drives based on different speedcontroller other than Proportional and Integrator (PI) controller. This paper presents a detailed comparativestudy on fuzzy rule-base in FuzzyLogicspeedController (FLC) for Dual Permanent Magnet Synchronous Motor (PMSM) drives. Two fuzzyspeed controllers which are standard and simplified fuzzyspeed controllers are designed and the results are compared and evaluated. The standard fuzzycontroller consists of 49 rules while the proposed controller consists of 9 rules determined by selecting the most dominant rules only. Both designs are compared for wide range of speed and the robustness of both controllers over load disturbance changes is tested to demonstrate the effectiveness of the simplified/reduced rule base. The developed FuzzyLogic model has the ability to learn instantaneously and adapt its own controller parameter based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.
Sensor less control of induction motor using vector control technique has been proposed. Sensor less control gives the benefits of vector control without using any shaft encoder. The mathematical model of the drive system has been developed. Vectorcontrolled induction motor by employing the different speed controllers like PI, sliding mode controller and fuzzylogic controllers is presented. The performance of the sliding mode controller and fuzzylogiccontroller for the indirect vectorcontrolled induction motor drive has been verified and compared with that of conventional PIcontroller performance. It can be concluded that the fuzzylogic controllers performance is better in comparison with that of PI and SMC in terms of the transient response. The dynamic performance of SMC is found to be the best out of the three controllers. The robustness of the SMC and Fuzzylogiccontroller during sudden changes in load has been seen but SMC gives better performance than fuzzylogiccontroller with load conditions. PIcontroller is very simple to implement, but its steady state response and dynamic performance are not very satisfactory. Its robustness to load disturbances is also relatively poor.
Brushless D.C (BLDC) synchronous motors have been used in various fields of industrial applications for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of drive control . Permanent Magnet Brushless DC (PMBLDC) motor is defined as a permanent magnet synchronous motor with a trapezoidal Back EMF waveform . BLDC motors do not have brushes for commutation. They are electronically commutated . For the variable speed applications of BLDC motor, Proportional, Integral and Derivative (PID) motor control is commonly used control .Because; it has simple design and ease of control. However, its performance depends on proportional, integral and derivative gains [5- 6]. When the operating condition changes, the re-tuning process of control gains is necessary for dynamically minimize the total controller error. The various algorithms are used to find optimal PID controller parameters such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) [7-10].Particle Swarm Optimization (PSO) and genetic algorithm (GA) is given based on population size, generation number, selection method, and crossover and mutation probabilities. There is no guarantee for finding optimal solutions for controllers within a finite amount of time. To overcome the problems in PID controller, fuzzylogiccontroller and hybrid fuzzy PID controllers can be designed for the speed control of BLDC motor. In this proposed research work, the speed control of BLDC motor was analyzed and its performance has been observed by using fuzzylogiccontroller and hybrid fuzzy PID [11- 13].The simulation results of two methods are studied and compared with conventional PIcontroller by using MATLAB/SIMULINK computational software. The simulation results of proposed controllers are used to show the abilities and shortcomings of conventional PIcontroller.
The speed control of IM issues are traditionally handled by fixed gain PI and PID controllers. However the fixed gain controllers are very sensitive to parameter variations, load disturbances etc. Thus, the controller parameters have to be continuously adapted. The problem can be solved by several adaptive control techniques such as model reference adaptive control, sliding mode control smc, variable structure control VSC and self tuning PIcontroller etc. The design of the entire above controller depends on the exact system mathematical model. However it is often difficult to develop a accurate mathematical model due to unknown load variation and unavoidable parameter variations due to saturation, temperature variations and system disturbance. To overcome the above problems, Fuzzylogiccontroller (FLC) is being used for motor control purpose. There is some advantage of fuzzylogiccontroller as compared to conventional PI, PID and adaptive controller such as it does not require any mathematical model, it is based on linguistic rules within IFTHEN general structure, which is the basic of the human logic.
Abstract—this paper presents an intelligent speed control system based on fuzzylogic for a voltage source PWM inverter- fed indirect vectorcontrolled induction motor drive. Traditional indirect vector control system of induction motor introduces conventional PI regulator in outer speed loop; it is proved that the low precision of the speed regulator debases the performance of the whole system. To overcome this problem, replacement of PIcontroller by an intelligent controller based on fuzzy set theory is proposed. The performance of the intelligent controller has been investigated through digital simulation using MATLAB-SIMULINK package for different operating conditions such as sudden change in reference speed and load torque. The simulation results demonstrate that the performance of the proposed controller is better than that of the conventional PIcontroller.
The performance of fuzzylogic based intelligent controller for the speed control of indirect vectorcontrolled Induction motor drive has been verified and results were compared with that of conventional PIcontroller performance. The simulation results obtained have confirmed the very good dynamic performance and robustness of the fuzzylogiccontroller during the transient and steady state period. It is concluded that the proposed intelligent controller has shown superior performance than that of the parameter fixed PIcontroller.
Induction motor drive draws heavy current during starting condition. The current is 4 to 7 times of rated current, if this current present in the motor for large time period not only it can damage insulation but conductors too. If this transient period for achieving rated speed is large it can causes above problems. The equipment which reduces the transient time of induction motor is controlled operation of Voltage Source Inverter but using this one; introduces harmonics in the machine and in system. These harmonics can cause overheating of the motor and supply system result in reduction in overall life span of motor, reduced efficiency, poor performance and unwanted failure of drive system causes economic Burdon on organization in form of less production. To solve these issues an attempt is made to make a comparativestudy on various open loop and close loop drive including PIcontroller & FuzzyLogiccontroller for determination of their advantages and limitations for any particular operation of drive.
The proposed work is all about the speed control of three stage induction magnetic motor utilizing PIcontroller & Fuzzy rationale controller is based on indirect vector control approach. The proposed control frameworks use fuzzy rationale controller to improve the execution of induction magnetic motor drives & likewise serves to accomplish accuracy in control. From the SIMULINK results, it is observed that the Fuzzy rationale controller shows better execution regarding rise time and consistent steady state reaction. The fuzzy rationale controller gives quick reaction to speed command than the PIcontroller. Thus the fuzzy rationale controller has demonstrated predominant element execution and superior dynamic performance furthermore power than that of PIcontroller.
This paper shows the Comparison & performance of conventional PIcontroller & Fuzzylogiccontroller by setting different Speed levels both increasing and decreasing then simulation results are obtained under different load conditions. The results shows that speed and torque responses are better in Fuzzylogic based controller as compared to PIcontroller. Under different load conditions the steady state is reached quickly in fuzzylogiccontroller without dropping more speed and a very slight delay. It can be also
ABSTRACT: Direct torque control is used over Field oriented control (FOC) because of its simple control structure and in steady state, transient state operating conditions shows better torque control. Direct torque control has the advantages like robust and fast torque responsive. But in low speed operation stator flux estimation raises difficulty due to improper working of an open loop voltage model observer and existence of an open loop integrator. Hence an adaptive flux observer is implemented which eliminates open loop integration, therefore improves the machine performance by minimizing stator current distortions, fast response of rotor speed, stator flux electro-magnetic torque without ripple, constant switching frequency. Voltage distortions are modelled using a non-linear inverter. In this paper a novel approach is seen where fuzzylogiccontroller is adapted which overcomes high torque ripples and improves the system performance. Simulation results are carried out for the proposed system.
an injection transformer is commonly termed as series filters (SAF). It acts as a controlled voltage generator. It has capability of voltage imbalance compensation, voltage regulation and harmonic compensation at the utility- consumer point of common coupling (PCC). In addition to this, it provides harmonic isolation between a sub- transmission system and a distribution system. The second unit connected in parallel with load, is termed as Shunt Active Filter. It acts as a controlled current generator. The shunt active filter absorbs current harmonics, compensate for reactive power and negative sequence current injected by the load. In addition, it controls dc link current to a desired value. In power line conditioner one more element is a dc link inductor, which acts as energy storage device. A small amount ofdc power supply is required to operate active power filter for harmonic compensation. The dc link inductor functions as dc power supply sources and hence does not demand any external power source. However, in order to maintain constant dc current in the energy storage element, a small fundamental current is drawn to compensate active filter losses [8-11].
This research paper has under development FTAG FIS, an adaptive speedcontroller for the PMSM using the Adaptive Neuro-Fuzzy Inference System. After The developed control algorithm has been verified through simulations to be not only more superior to the conventional PI controllers, especially during load torque disturbances, but also to be robust during speed changes, speed disturbances, as well as variations in the motor parameters it also increase the efficiency. The control algorithm design methodology that has been presented in this paper shows its effectiveness in eliminating the need for the control designer to adjust the input and output gains of the fuzzycontroller manually by trial and error. On top of that, it has disguised the non-linear characteristic of the load torque into one that is linearly related to the output gain of the developed fuzzycontroller. The FTAG FIS algorithm guarantees the closed loop performance of the PMSM even when the motor is subjected to significant and unpredictable motor parameter variations and load torque disturbances at both low and high speeds. An outstanding advantage of the FTAG FIS algorithm for the PMSM is that it uses only15 rules in its rule base, which is less than half the number of rules that is being used in many other fuzzy controllers that have been proposed in the literature. With fewer rules, the FTAG FIS is not only more easily implemented, but will have shorter execution time. Suggestions for further work include a real time implementation of the algorithm onto the motor to verify whether there is a close agreement between the theoretical and experimental results and modifying the current FTAG FIS algorithm to incorporate one, and
Reliable LQ Fuzzy Control for Continuous Time Non Linear Systems with Actuators Fault using multiple Lyapunov functions, an improved linear matrix inequality (LMI) method for the design f LQ fuzzy controllers is investigated, which reduces the conservation of using a single Lyapunov function. A suboptimal reliable LQ fuzzycontroller is given by means of an LMU optimization procedure, which can not guarantee the stability of the closed lop overall fuzzy system for all cases. Finally, a numerical simulation on the chaotic Lorenz system is given to illustrate the application of the proposed design method.
The proposed scheme for the Sensor less PMSMdrive fed by a Zeta based PFC converter operating in DICM mode is shown in Fig.2.1.The front end Zeta DC-DC converter maintains the DC link voltage to a set reference value. Switch of the Zeta converter is to be operated at high switching frequency for effective control and small size of components like inductors. A sensor less approach  is used to detect the rotor position for electronic commutation.A high frequency MOSFET of
computing techniques is fuzzy-logic. Fuzzylogic is a form of many-valued logic or probabilistic logic; it deals with reasoning that is approximate rather than fixed and exact. Compared to traditional binary sets (where variables may take on true or false values) fuzzylogic variables may have a truth value that ranges in degree between 0 and 1. Fuzzylogic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions. The term "fuzzylogic" was introduced with the 1965 proposal of fuzzy set theory by Lotfi A. Zadeh. Fuzzylogic has been applied to many fields, from control theory to artificial intelligence. Fuzzy logics however had been studied since the 1920s as infinite-valued logics notably by Łukasiewicz and Tarski; it is a popular misconception that they were invented by Zadeh. These are referred as intelligent controllers which we have been proposed for speed control of induction motor. Thos controllers are associated with adaptive gains due to fuzzy inference and knowledge base. As a result, they can improve torque disturbance rejections in comparison with best trial-and-error PI controllers. Nonetheless, no performance advantages of intelligent controllers in combination with a PIcontroller are investigated. Motivated by the successful development and application we propose a hybrid PI + Fuzzycontroller consisting of a PIcontroller and a fuzzylogiccontroller (FLC) in a serial arrangement for speed control of induction motor more specifically, direct field-oriented induction motor drives. The Ziegler-Nichols (Z-N)) method is adopted for designing a PIcontroller because its design rule is simple and systematic. We next design a FLC carrying out fuzzy tuning of the output of the Z-N PIcontroller to issue adequate torque commands. The results show that the incorporation of the proposed controller into the DFOIM drives can yield superior and robust variable speed tracking performance [2, 3, 7, 33].
instrumentation applications, particularly in robotics and computer peripherals. The speed of PMDC motor can be controlled by many controllers. In this paper PID, pole placement and Fuzzycontroller are used .The advantages and disadvantages of each controller for different conditions under no load, load and disturbance conditions using software MATLAB are being discussed. Pole placement controller can be employed to obtain speed control of PMDC motor. An addition of integrator reduced the noise disturbances in pole placement controller and this makes it a good choice for industrial applications. An intelligent controller is introduced with a DC chopper to make the PMDC motor speed control smooth and almost no steady state error is observed. To prove the robustness of the proposed FuzzyPIcontroller, three different controllers are compared and concluded from the results that Fuzzycontroller performs to PID controller in terms of steady state error and smooth step response.
mechanism (controller) widely used in industrial control systems. A PID controller calculates an error value as the difference between a measured process variable and a desired set point. The controller attempts to minimize the error by adjusting the process through use of a manipulated variable. The PID controller algorithm involves three separate constant parameters, and is accordingly sometimes called three- term control: the proportional, the integral and derivative values, denoted P, I, and D. Simply put, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction of future errors, based on current rate of change. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve, a damper, or the power supplied to a heating element.
Indirect vector control principle was introduced by Blaschke in 1972. It states that the flux and torque can be controlled independently. It consists of dynamic d-q model which consists of voltage source inverter, flux calculation, theta calculation, current and voltage sensing elements. Fig. 1 shows a d-q model for indirect vector control system. Usually, a vector control technique provides the application of induction motor drives for improved and high performance. Indirect vector control system is somewhat similar as that of direct vector control except that the rotor angle θe is generated in an indirect manner using the measured speed ω r and slip speed ω sl . Following dynamic equations are
The future work of this is to control the speed of BLDC motor using intelligent fuzzylogiccontroller. Thus the control using FLC will be more effective than this PIcontroller. 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.