This paper analyze design of space vector modulation based direct torque controlled (**DTC**-**SVM**) **five**-**phase** **induction** **motor** incorporated with **ANN** **controller** and has given a comparison with **PI** **controller**. This **ANN** **controller** is employed to improve the control performance parameters such as reducing torque & flux ripple, reducing the settling and rise time compare to that of **PI** **controller**. The modelling and simulation is performed in MATLAB, it shows that with intelligent controlled the performance parameters have been improved and drive system could be operated at low speed. The proposed model is less complex, require a single **ANN** **controller** for decoupled flux and torque controlled.

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In the case of current harmonics the Shunt active power filter appears as the best dynamic solution for harmonic compensation. The principle operation of Shunt active power filter is to generate compensating currents into the power system for cancelling the current harmonics contained in the non-linear load current. This will result in sinusoidal line currents and unity power factor in the input power. This paper offers a good way to optimize the performance of Shunt active power filter [1]. By using three different controllers Fractional **PI**, **PI** and **ANN** Controllers. The overall dynamic performance of the Shunt active power filter has been increased. In this paper, a multi-range Fractional-order repetitive control **scheme** proposed for enhancing Active Power Filter(APF) performance where is assumed to vary in a range between 0.1 to 1. Depending on the values of the order ʎ, and **phase** and gain margin, different stability regions have been obtained. It has been seen, Fractional order **pi** **controller** is less sensitivity to parameter variation than classical **PI** **controller**, as the Fractional order **PI** **controller** makes use of fractional order derivatives and integrals because of which overall dynamic performance is improved [2]. The performance of the proposed controllers has been investigated, compared and analysed under different testing scenario and different simulation results are implemented. All the three **controller** simulation results illustrates that the proposed technique **ANN** **Controller** are more promising than Fractional **PI** and **PI** **Controller** because of its high speed recognition, learning ability and ability to adapt themselves in any system .

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At the inverter side, extinction angle (γ) control and current control is implemented. The combination of Constant Current Control (CCC) and Voltage Dependent Current Order Limiter (VDCOL) is used and it is passed through **PI** **controller**. By comparing Voltage Dependent Current Order Limiter (VDCOL) output and external reference, we get reference limit for current control. The angle order is obtained by subtracting the measured current and the reference limit. For inverter, gamma angle order is obtained from another **PI** **controller** which used in γ control. Firing instant is calculated by comparing two angle orders and minimum value is selected for measuring the firing instant. The time between thyristor current extinction and thyristor commutation voltage becomes positive which is denoted by gamma and it expressed in degree. To change the time value as electrical degree, we used the system frequency. The current threshold gives the current extinction time. At the converter transformer, three-**phase**-to-ground AC voltages gives six commutation voltages and six thyristors are gives the six gamma angles. In control action minimum value of gamma is considered. To obtain error signal by comparing gamma output and reference gamma. Gamma output is obtained from a 12- pulse converter which gives two values of gamma and selects a minimum value of gamma output for consideration of error signal. To obtain firing pulse by comparing firing angle order which is obtained from the constant current **controller** and from the constant extinction angle **controller** and select a minimum value for valves. To measure the DC voltages and current use proper voltmeter and ammeter. To measure three-**phase** voltages and currents use three-**phase** VI measurement blocks. “From” and “Goto” blocks are used for signal routing and scope is used to display.

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The simulation results show that the performance of the Artificial Neural Network is superior compared to that of the conventional **PI** **controller**. The proposed system of improved UPQC has the advantages of high reactive power compensation, power quality compensation, and critical load compensation. Applications of the proposed system are Smart grids, Microgrids, and distributed generation and energy storage system to better deal with inherent variability of renewable resources such as wind and solar.

For electrical drives good dynamic performance is mandatory so as to respond to the changes in command speed and torques, so various speed control techniques are being used for real time applications. The speed of a dc **motor** can be controlled using various controllers like **PI**- **Controller**, Artificial Neural Network (**ANN**) **controller**. **ANN** theory is recently getting increasing emphasis in process control applications. The paper describes application of **ANN** in a speed control system that uses a **phase**-controlled bridge converter and a separately excited DC machine. The **ANN** **controller** for current and speed loops are implemented in MATLAB/SIMULINK, replacing the conventional Proportional-Integral (**PI**) control method. The simulation study indicates the superiority of artificial neural network control over the conventional control methods. This control seems to have a lot of promise in the applications of power electronics.

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This paper proposes the neural network solution to the indirect vector control of three **phase** **induction** **motor** including an adap- tive neuro fuzzy **controller**. The basic equations and elements of the indirect vector control **scheme** are given. The proposed con- trol **scheme** is realized by an adaptive neuro-fuzzy **controller** and two feed forward neural network. The neuro-fuzzy **controller** in- corporates fuzzy logic algorithm with **five** layer artificial neural net- work (**ANN**) structure. The conventional **PI** **controller** is replaced by adaptive neuro-fuzzy inference system (ANFIS) which tunes the fuzzy inference system with hybrid learning algorithm. The two feed forward neural network are used as estimator, learned by the Levenberg-Marquardit algorithm with data taken from **PI** control simulations. The performance of proposed **scheme** is investigated at different load and speed conditions. The result of the proposed **scheme** are compared with **PI** **controller**. The simulation study in- dicates the robustness and suitability of drive for high performance drive applications.

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In this paper an adaptive fuzzy **PI** **controller** along with the SVPWM technique is applied to inverter. Fuzzy **PI** **controller** is used to achieve precision torque control and minimize torque ripple. When fuzzy logic is used for the on-line tuning of the **PI** **controller**, it receives fuzzy values of the torque error and change of torque error. Its output is updating in the **PI** **controller** gains based on a set of rules to maintain excellent control performance. The proposed technique is simulated in simulink to validate the performance of the algorithm.

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An adaptive flux observer with space vector modulation (**SVM**) technique is used to reduce the ripples to obtain constant switching frequency to the inverter and a torque ripple with reduced flux. **DTC**-**SVM** **scheme** is developed to reduce the ripples and also which an advantage of minimizing stator current distortions, fast response of rotor speed, stator flux electro-magnetic torque without ripples and constant switching frequency is obtained. In this paper fuzzy logic **controller** is adapted to improvise the system performance as an innovative technology with engineering expertise’s and enhances conventional system by reducing torque ripple and constant switching frequency is maintained.

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B. **DTC**-**SVM** Based on Input-Output Linearization The **DTC**-**SVM** **scheme** is developed based on the IM torque and the square of stator flux modulus as the system outputs; stator voltage components defined as system control inputs and stator currents as measurable state variables.

Brushless DC (BLDC) motors are one of the most interesting motors, not only because of their efficiency, and torque characteristics, but also because they have the advantages of being a direct current (DC) supplied, but eliminating the disadvantages of using Brushes. BLDC motors have a very wide range of speed, so speed control is a very important issue for it. There are a lot of parameters which need to be in focus while talking about a speed **controller** performance like starting current, starting torque, rise time, etc. There are two main methods for controlling the speed, PID Controllers, and Fuzzy **PI** controllers. Both are different in complexity and performance. In this paper, the **PI** and Fuzzy **PI** speed controllers for the BLDC motors will be proposed. A simulation study is conducted to evaluate the efficiency of the proposed speed controllers. Further, a comparative study is performed to validate the system effectiveness.

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V. S PACE VECTOR PULSE WIDTH MODULATION The basic idea of the SVPWM is bought from the operation of the **induction** **motor**. Traditionally in the **induction** **motor** the transformation of the three **phase** stator current into the two **phase** rotor flux is the basic formation of the space vector modulation. Space vector modulation (**SVM**) is an algorithm for the controlling the switching operation of the inverter. The space vector modulation mostly creates the AC waveforms to operate a 3-**phase** AC drives at variable speed. The space vector modulation it used for the different controlling operations and for computational requirements. **SVM** utilize the available DC bus voltage by 15 % more than SPWM. One of the main research area for the development of high voltage and reduction of total harmonic distortion (THD) created by the rapid switching. Its Treats the sinusoidal voltage as constant amplitude vector rotating at constant frequency, it directly uses the control variable given by the control system and identifies each switching vector as a point in complex space. Sector identification and triangle determination is to calculate the switching intervals for all vectors make **SVM** method quite complicated.

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Three no-load **induction** **motor** tests were made. The first one was the response to a torque step of 12.2 Nm which is shown in Figure 7. The response of the **DTC** with complex **controller** presented a slightly better per- formance in transient and steady state when such re- sponse is compared with the response of **DTC** with **PI** **controller**. It can be observed that the response time is 25 ms and the reference is followed with a small oscilla- tion. This oscillation occurs due to the natural lack of accuracy in the measurements of currents and voltages. In the second test the speed varies in forward and re- versal operation and the result is presented in Figure 8. The speed changes from 13 rad/s to -13 rad/s in 1 s and the complex gain is not changed during the test. This result confirms the satisfactory performance and the ro- bustness of the **controller** due to the fact that the the speed reaches the reference in several conditions. The responses of the **DTC** with complex **controller** and of the **DTC** with **PI** **controller** have the same performance in transient and steady state. The small error occurs due the natural lack of accuracy in the measurement of the speed.

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Many circuit simulators like PSPICE, EMTP, MATLAB/ SIMULINK incorporated these features. The advantages of SIMULINK over the other circuit simulator are the ease in modelling the transients of electrical machines and drives and to include controls in the simulation. To solve the objective of this paper MATLAB/ SIMULINK software is used. The superior control performance of the proposed **controller** is demonstrated at SIMULINK platform using the fuzzy logic tool box [5] for different operating conditions.

This study deals with a reduced sensor configuration of a power factor correction (PFC) based zeta converter for **induction** **motor** for low power applications. The speed of the **Induction** **motor** is controlled by varying the dc-link voltage of the voltage source inverter (VSI) feeding **induction** **motor** drive. A low-frequency switching of the VSI is used for achieving the electronic commutation of **induction** **motor** for reduced switching losses. The PFC-based zeta converter is designed to operate in discontinuous inductor current mode; thus utilizing a voltage follower approach which requires a single voltage sensor for dc-link voltage control and PFC operation. The proposed drive is designed to operate over a wide range of speed control with improved power quality at ac mains and the results are verified through numeric simulation using MAT Lab Simulink.

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The Vector Oriented **Controller** (VOC) is also known as Field Oriented **Controller** (FOC). The main objective of this control method is to independently control the torque and the flux as in **induction** machines. This is done by choosing a d-q rotating references frame synchronously with the rotor flux space vector. Once the orientation is correctly achieved, the torque is controlled by the torque producing current which is the q-component of the stator current space vector. At the same time, the flux is controlled by the flux producing current, which is the d-component of the stator current space vector. Indirect field-oriented control, both the instantaneous magnitude and position of the rotor flux are supposed to be precisely known. Crucial to the success of this well known control technique is a priori knowledge of the rotor electrical term constant which varies with temperature, frequency and saturation.This method of **induction** machine achieves decoupled torque and flux dynamics. This is achieved by orthogonal projection of the stator current into a torque-producing component and flux-producing component. This technique is performed by two basic methods. Direct and indirect vector control. With direct field orientation, the instantaneous value of the flux is required and obtained by direct measurement using flux sensors or flux estimators, whereas indirect field orientation is based on the inverse flux model dynamics and there are three possible implementation based on the stator, rotor, or air gap flux orientation. The rotor flux indirect vector control technique is the most widely used due to its simplicity. FOC methods are attractive but suffer from one major disadvantage. They are sensitive to parameter variations such as rotor time constant and incorrect flux measurement or estimation at low speeds [3], [4]. Basic block diagram of FOC is shown in figure 1 [2].

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The process industry implements many techniques with certain parameters in its operations to control the working of several actuators on field. Amongst these actuators, DC **motor** is a very common machine. The angular position of DC **motor** can be controlled to drive many processes such as the arm of a robot. The most famous and well known **controller** for such applications is PID **controller**. It uses proportional, integral and derivative functions to control the input signal before sending it to the plant unit. In this paper, another **controller** based on Artificial Neural Network (**ANN**) control is examined to replace the PID **controller** for controlling the angular position of a DC **motor** to drive a robot arm. Simulation is performed in MATLAB after training the neural network (supervised learning) and it is shown that results are acceptable and applicable in process industry for reference control applications. The paper also indicates that the **ANN** **controller** can be less complicated and less costly to implement in industrial control applications as compared to some other proposed schemes.

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The AND gate used in the circuit is 7408N. The input of the gate is derived from the output of the comparator and that of the 555 timer. The AND gate is used to eliminate the negative pulse train. The output of the AND gate is shown below. These are train of pulses and are used for triggering the TRIAC. Thereby by the TRIAC will be conducting and the supply is connected to the **induction** **motor**.

Figure 11 and Figure 12 shows a simulation and experimental results of the torque ripple for **DTC**- MLI with hysteresis-based **controller** with ΔT = 0.9N.m (or 10% of the rated torque) and **DTC**-MLI with **PI**- CSF **controller**. Based on the experimental results, its clearly indicates that the reduction of torque ripple as much as 26% in the **DTC**-MLI with **PI**-CSF **controller** compared to the **DTC**-MLI with hysteresis-based **controller**. Using the **PI**-CSF **controller**, the designated **controller** is properly monitored and corrected the level of errors to ensure the **PI** **controller** signal is within the appropriate carrier level hence suitable voltage vector is selected either to increase or decrease the torque and been applied consecutively within a carrier waveform period. However by using the hysteresis-based **controller**, the voltage vector is chosen based on the comparison of the raw error signal with the hysteresis band which the selected voltage vector for increasing or decreasing the torque is applied for the entire switching period.

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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.

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The comparison of the three algorithms for the **controller** design and tuning discussed in this paper is made for various operating points. The speed response of the drive changes with the change in the operating point or with the change in the system parameters. The performance of the three algorithms is compared in terms of the transient parameters of the system such as the peak overshoot and the settling time. The values of these transient parameters in turn are used to evaluate the numeric value of the objective function. Lower numeric values of the objective function ensure good response. Finally the percentage improvement in the dynamic response of the drive with respect to the conventional methods is discussed in Table 2. Thus from the above computations and graphical analysis it’s clear that the first and second algorithm as discussed in sections IV and V would yield same results but however vary significantly in the speed of response. INPUT 1

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