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 **FUZZY** **logic** **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 **FUZZY** **logic** **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.

Show more
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 **speed** **controller** other than Proportional and Integrator (**PI**) **controller**. This paper presents a detailed **comparative** **study** on **fuzzy** rule-base in **Fuzzy** **Logic** **speed** **Controller** (FLC) for Dual Permanent Magnet Synchronous Motor (**PMSM**) drives. Two **fuzzy** **speed** controllers which are standard and simplified **fuzzy** **speed** controllers are designed and the results are compared and evaluated. The standard **fuzzy** **controller** 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 **Fuzzy** **Logic** 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.

Show more
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. **Vector** **controlled** induction motor by employing the different **speed** controllers like **PI**, sliding mode **controller** and **fuzzy** **logic** controllers is presented. The performance of the sliding mode **controller** and **fuzzy** **logic** **controller** for the indirect **vector** **controlled** induction motor **drive** has been verified and compared with that of conventional **PI** **controller** performance. It can be concluded that the **fuzzy** **logic** 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 **Fuzzy** **logic** **controller** during sudden changes in load has been seen but SMC gives better performance than **fuzzy** **logic** **controller** with load conditions. **PI** **controller** 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.

Show more
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 [1]. Permanent Magnet Brushless DC (PMBLDC) motor is defined as a permanent magnet synchronous motor with a trapezoidal Back EMF waveform [2]. BLDC motors do not have brushes for commutation. They are electronically commutated [3]. For the variable **speed** applications of BLDC motor, Proportional, Integral and Derivative (PID) motor control is commonly used control [4].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**, **fuzzy** **logic** **controller** 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 **fuzzy** **logic** **controller** and hybrid **fuzzy** PID [11- 13].The simulation results of two methods are studied and compared with conventional **PI** **controller** by using MATLAB/SIMULINK computational software. The simulation results of proposed controllers are used to show the abilities and shortcomings of conventional **PI** **controller**.

Show more
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 **PI** **controller** 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, **Fuzzy** **logic** **controller** (FLC) is being used for motor control purpose. There is some advantage of **fuzzy** **logic** **controller** 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**.

Show more
Abstract—this paper presents an intelligent **speed** control system based on **fuzzy** **logic** for a voltage source PWM inverter- fed indirect **vector** **controlled** 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 **PI** **controller** 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 **PI** **controller**.

Show more
The performance of **fuzzy** **logic** based intelligent **controller** for the **speed** control of indirect **vector** **controlled** Induction motor **drive** has been verified and results were compared with that of conventional **PI** **controller** performance. The simulation results obtained have confirmed the very good dynamic performance and robustness of the **fuzzy** **logic** **controller** 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 **PI** **controller**.

Show more
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 **comparative** **study** on various open loop and close loop **drive** including **PI** **controller** & **Fuzzy** **Logic** **controller** for determination of their advantages and limitations for any particular operation of **drive**.

Show more
The proposed work is all about the **speed** control of three stage induction magnetic motor utilizing **PI** **controller** & **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 **PI** **controller**. Thus the **fuzzy** rationale **controller** has demonstrated predominant element execution and superior dynamic performance furthermore power than that of **PI** **controller**.

Show more
This paper shows the Comparison & performance of conventional **PI** **controller** & **Fuzzy** **logic** **controller** 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 **Fuzzy** **logic** based **controller** as compared to **PI** **controller**. Under different load conditions the steady state is reached quickly in **fuzzy** **logic** **controller** without dropping more **speed** and a very slight delay. It can be also

Show more
15 Read more

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 **fuzzy** **logic** **controller** is adapted which overcomes high torque ripples and improves the system performance. Simulation results are carried out for the proposed system.

Show more
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].

Show more
This research paper has under development FTAG FIS, an adaptive **speed** **controller** 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 **fuzzy** **controller** 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 **fuzzy** **controller**. 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

Show more
12 Read more

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 **fuzzy** **controller** 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.

Show more
24 Read more

The proposed scheme for the Sensor less **PMSM** **drive** 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 [12] is used to detect the rotor position for electronic commutation.A high frequency MOSFET of

computing techniques is **fuzzy**-**logic**. **Fuzzy** **logic** 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) **fuzzy** **logic** variables may have a truth value that ranges in degree **between** 0 and 1. **Fuzzy** **logic** 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 "**fuzzy** **logic**" was introduced with the 1965 proposal of **fuzzy** set theory by Lotfi A. Zadeh. **Fuzzy** **logic** 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 **PI** **controller** are investigated. Motivated by the successful development and application we propose a hybrid **PI** + **Fuzzy** **controller** consisting of a **PI** **controller** and a **fuzzy** **logic** **controller** (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 **PI** **controller** because its design rule is simple and systematic. We next design a FLC carrying out **fuzzy** tuning of the output of the Z-N **PI** **controller** 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].

Show more
14 Read more

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 **Fuzzy** **controller** 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 **Fuzzy** **PI** **controller**, three different controllers are compared and concluded from the results that **Fuzzy** **controller** performs to PID **controller** in terms of steady state error and smooth step response.

Show more
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.

Show more
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

Show more
10 Read more

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.

Show more