Generalized Predictive Control of Multi-Phase Induction Machine Supplied by Multi-Level Inverter

Finite Control Set-Model Predictive Speed Control for Induction Motors with Optimal Duration

The wide availability of microprocessors with much higher computational power capabilities has encouraged many researchers during the last decade to introduce the predictive control techniques to the field of power electronics and motor drives. One technique of predictive control, which is called model predictive control, is introduced in this paper and its application to control a five-phase induction motor is also presented. The mathematical formulation of both model predictive torque control and model predictive speed control algorithms are explained, which shows that the algorithms' equations are intuitive so they can be easily understood and implemented. Since a load torque estimate is needed in the predictive speed controller, an observer based on the minimal-order Gopinath's method is designed to get the load torque value. A comparison between the two techniques is carried out based on a simulation study. Also, the simulation results show the effectiveness of the predictive control in achieving a high dynamic performance control of multiphase induction motor drives.

In this article, sensorless finite control set model predictive speed control of induction motor using adaptive full-order observer is proposed. Firstly, finite control set model predictive speed control is introduced. It is composed of direct speed control and model predictive control, which gets over the limitations of cascaded structure and obtains high dynamics response. Due to lack of speed loop, stator flux linkage, rotor speed and stator current are taken as the control targets in the cost function. Then, adaptive full-order observer is used to estimate rotor speed and stator flux linkage to improve the reliability and keep the system cost down. Finally, the simulation results indicate that the proposed method has well dynamic performance in the wide speed range.

This paper looks at design of a controller for an induction motor electrical drive useful for high speed railway applications. The design of the controller is based on the cascade control mode using the standard model of the motor referred to d-q axes fixed with the rotor flux vector. It is assumed that the speed and the rotor flux are the variables to be controlled and their dynamics must be decoupled, which is accomplished by means of a nonlinear compensator, which gives decoupling of the direct and in-quadrature currents. According to the aforementioned cascade control mode, the bandwidth of the current inner control loops must be selected higher than that of the speed and rotor flux outer control loops. To take into consideration the remarkable uncertainties in the knowledge of the inertia coefficient, adaptive control techniques are used to design the speed control loop. To cope with uncertainties in electromagnetic parameters, robust control techniques are used for designing the rotor flux, the direct, and the in-quadrature current loops. Simulation experiments are displayed to test the entire controller.

Recently, many studies have been performed for the variable speed control of an induction motor. Direct torque control (DTC) is an emerging technique for the variable speed control of PWM inverter driven induction motors. DTC allows the direct control of stator flux and instantaneous torque through a simple algorithm. In this paper, an ASIC design technique using VHDL is applied to DTC based speed control of an induction motor. ASIC for DTC based speed control is designed through the descriptions of coordinate transformation, speed controller stator flux and torque estimator, stator flux and torque controller stator flux position detector, FSM (finite state machine) and inverter voltage switching vector. Finally the above system has been implemented on the FPGA (XC4052XL-PG411). Simulation and experiments have been performed to verify the performance of the designed ASIC.

Cascaded nonlinear predictive controller for induction motor drive is presented. The load torque, considered as an unknown disturbance, is rejected using a disturbance observer. First, a nonlinear multivariable predictive controller is applied to track electromagnetic torque and rotor flux norm trajectories. Then, a speed predictive control strategy is carried out from the electromechanical equation of the machine. Both controllers are applied in a cascade structure to induction motor. The prediction model is carried out via Taylor series expansion using Lie derivatives for nonlinear model. The derived predictive law minimizes a quadratic performance index of the predicted tracking error for multivariable system and a predicted error for speed control. The implementation of the cascaded control law does not need an on line optimization and the tracking of desired trajectories is achieved successfully. The load torque observer, derived from the speed predictive control, is simplified to a PI structure. This observer structure guarantees the disturbance rejection and the robustness to parameters variations. Simulation results show the high performance of the proposed control scheme.

Because of its low maintenance and robustness, induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy logic controller. Induction motor is modeled in synchronous reference frame in terms of dq form. The speed control of induction motor is the main issue achieves maximum torque and efficiency. Two speed control techniques, Scalar Control and Indirect Field Oriented Control are used to compare the performance of the control system with fuzzy logic controller. Indirect field oriented control technique with fuzzy logic controller provides better speed control of induction motor especially with high dynamic disturbances. The model is carried out by using Matlab/Simulink computer package. The simulation results show the superiority of the fuzzy logic controller in controlling three-phase induction motor with indirect field oriented control technique.

Induction motor is a cast of alternating current motor where charge endures allotted to the rotor close-at-hand deputation of conductive charge. These motors are broadly applied in industrial claim due to they are arduous along with adhere no contacts. The speed controller of deltoid phase induction motor is applied to alleviate the aberration of speed. The central constructivist of this paper is to accrue the performance of speed sensorless control of three phase induction motor. To increase its performance, this paper presents a modified method for speed controller of an indirect vector-controlled induction motor drive using cloud computing technique. Our methodology depends on speed sensorless scheme to obtain the speed signal feedback; the speed estimator is based on model reference adaptive control that uses the stator current and rotor flux as state variables for estimating the speed. In this method, the stator current error is represented as a function of first degree of the estimated speed error. An analysis and simulation of the tried algorithm is birthed and applied easing a TMS320C31 floating-point notational alert Processor. And accumulate the action of the three phase induction motor we conceived our appraisals affixed to the accountant based on cloud computing tactics. This intelligent policy uses the guidelines of the speed controller efficiently. Simulation and experimental results depicted that the motor speed is decelerated articulately to destine its illusion apprise without above and inferior smack and with about zero steady state error. The apprised accelerate alert and its dispatching buoy amassed off line from burlesque. After effects display an advantageous affinity among the accounted speed alert and it's dispatching allocated as well as aped speed flares.

This paper presents a speed and position sensorless permanent magnet synchronous motor (PMSM) drive using simple and robust model reference adaptive control (MRAC) for light electric vehicle (LEV) application. The sensorless speed and position estimator consists the flux and torque based MRAC, which improves the performance during dynamic as well as steady state condition for wide range of speed operation. Mostly, PMSM drive uses field oriented control for the closed loop speed control operation. Generally, PI controller is used speed and current controller. However, the PI controller gain requirement often changes during various driving conditions of the vehicle. Therefore, this work is focused to eliminate fixed gain PI controller by utilizing online self-tuned PI speed controller and PMSM model based predictive current controller. The self-tuning approach of PI controller for the speed control loop uses the principle of adaptive interaction, which performs tuning by minimizing error objective function. The predictive current controller eliminates the need of modulator and incorporates the inverter nonlinearities. In addition to this, the sensorless approach reduces the complexity and cost of the drive. The combined system of improved controllers and sensorless estimator improves the drive performance for wide range of speed operation.

This paper attempts to obtain optimum performance for multi-level inverters by incorporating optimization techniques. The inverter performance is controlled via proportional-plus-integral controller. The parameters of the controller are obtained by implementing optimization techniques to enhance the performance of the controller, thereby promoting the power quality. Here, the ‘Differential Evolution’ and ‘Gravitational Search Algorithm’ are used separately to optimize the controller parameters. The Gravitational Search Algorithm is further employed to obtain the optimum proportional-plus-integral controller parameters for closed loop speed control of induction motor drive.

An improved finite-set predictive thrust control (FSPTC) is proposed in this paper, in which the fuzzy logic control (FLC) is applied instead of PI control in both speed control loop and adaptation mechanism of the model reference adaptive system (MRAS) with a lower number of membership functions to reduce the computation time. The MRAS observer based on primary flux linkage is implemented to estimate the linear speed. The output of the FLC serves as the reference thrust of the FSPTC while as speed in the MRAS observer. The developed control targets are to improve the characteristics of the drive system, where a significant ripple reduction in thrust and primary flux linkage, fast dynamic response, and lower cost can be guaranteed. Comprehensive simulation results have demonstrated the effectiveness of the proposed method based on one arc induction machine with 3kW rated power.

A global artificial neural network (ANN), algorithm for on-line speed control of a three-phase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the IM. The IM model includes three input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and constant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed ANN using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have been carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm.

This paper presents a hybrid wavelet-fuzzy based multiresolution (MR) controller for robust speed control of induction motor. The discrete wavelet transform (DWT) is used to decompose the error between the actual speed and command speed of the induction motor drive in to different frequency components. A self-tuning fuzzy logic is used for online tuning of the controller parameters. The proposed controller has the ability of meeting the speed tracking requirements in the closed loop system. The complete indirect field oriented control scheme incorporating the proposed wavelet-fuzzy based MR controller is investigated theoretically and simulated under various dynamic operating conditions. Simulation results are compared with that of conventional PI controller and fuzzy based PI controller. The speed control scheme incorporating the proposed controller is implemented in real time using the digital processor (DSP) control board. Simulation and experimental results validates the effectiveness of the proposed controller over conventional controllers and proves to be more suitable for high performance applications.

This paper proposes a design process of speed controller for induction motor using tabu search algorithm. The proposed process has two parts. The first part is optimization of the initial parameters of input-output. And the second part is changing parameters of input-output using tabu search. Proposed tabu search is improved by neighbor solution creation using Gaussian random distribution. In order to see the usefulness of the proposed method, it is applied to the sensorless speed controller of an AC induction motor system. The performance of this approach is verified through simulation.

This paper proposes the speed control of induction motor (IM) fed by wind turbine using imperialist competitive algorithm (ICA). The wind turbine plays as a prime mover to a connected direct current (DC) generator. Pulse width modulation (PWM) is used to get three-phase alternating current (AC) voltage from the output of DC generator. The proposed design problem of speed controller is established as an optimization problem. ICA is adopted to search for optimal controller parameters by minimizing the time domain objective function. The behavior of the proposed ICA has been estimated with the behavior of the conventional Ziegler–Nichols (ZN) and genetic algorithm (GA) to prove the superiority of the proposed ICA in tuning proportional plus integral (PI) controller. Also, the behavior of the proposed controller has been tested over a wide range of operating conditions. Simulation results confirm the better behavior of the optimized PI controller based on ICA compared with other algorithms.