A novel speed control technique for single-phase induction motor

Induction motor is very popular in industrial applications due to simple construction, low cost, and cheap maintenance costs. Speed control of induction motor requires frequency and magnitude voltage control. Some Volt/Hz control strategies are applied in speed control of induction motor with various methods such as direct torque control, vector control, harmonic elimination. These methods are very popular but require complex calculations. This paper discusses a simple method to operate an induction motor using the SPWM Volt/Hz, by using a lookup table method. This method contains important numbers as a reference for sinusoidal reference signal used to produce SPWM signal by comparing reference signals and triangular wave carrier signals from dsPIC microprocessor. SPWM Volt/Hz is used because the manufacturing process does not require complex calculations, easy to operate and reliable for induction motor speed operations. To prove that SPWM Volt/Hz can apply on speed control of induction motor, analysis simulation and experimental works were done. They show the proposed control strategy is capable to operate induction motor at the proper speed.

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.

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 research paper presents a novel method for the speed control of induction motors without using a speed sensor. The rotor speed can be accurately computed using an optimal control observer named the Kalman filter designed in this research paper. This replaces a speed sensor and eliminates the difficulty of the sensor installation in many applications. This research paper presents an advanced field oriented control of induction motors, based on a specific d-q coordinate model with the d-coordinate chosen to be in line with the rotor flux and the q-coordinate chosen to be 90⁰ lagging. The position of the rotor flux can be accurately computed using the Kalman filter. This eliminates the position sensors required accurately to monitor the flux. This research paper shows that as a result of this specific d-q transformation, the motor torque is proportional to the product of the rotor flux and the q-coordinate stator current. This significantly simplifies the induction motor control, such that the rotor flux is simply controlled by regulating the flux-related d-coordinates stator current and the motor torque is controlled just by regulating the q-coordinate stator current. This research paper presents a computational efficient recursive algorithm for Kalman filter which is specifically designed for the induction motor control. The Kalman filter provides the minimum variance state estimation and tolerates induction motor modelling and measurement errors. The Kalman filter can process all available measurements regardless of their precision (only two input current measurements required for the motor control), and provides a quick and optimal estimate of the variables of interest (the rotor speed and flux selected as outputs), as well as achieves a fast convergence. This research paper presents the digital signal processor (DSP) implementation of the field oriented control of induction motors using Kalman filter. The hardware requirements and all software modules are detailed. The experimental verification of the control method designed in this research paper is provided. Typical measurements are given to demonstrate the efficiency of the novel control presented in this research paper.

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.

In this paper, the model predictive control (MPC) technique for controlling the speed of sensorless induction motor drive is presented. To decrease the cost and increase the reliability and affectivity of the motor drive, a full order observer for speed estimation based on the model reference adaptive system MRAS is used to estimate the rotor speed. The adaptive full-order observer based on IM equation is used to estimate stator currents and rotor flux. Lyapunov's stability criterion is employed to estimate rotor speed based on the error between the actual and estimated currents. The same algorithm deduced from Lyapunov's stability criterion is given to estimate the stator resistance, which results in the speed estimation error. A simple model of induction motor is employed in the MPC structure so as to minimize the computational load. The MPC controller design is taken into account the effect of uncertainty due to motor parameters variation and load disturbance could be reduced. The effectiveness of the proposed scheme has been successfully verified through simulations. The results proved that the induction motor with the MPC speed controller has superior transient response, and good robustness in face of uncertainties including load disturbance. Moreover, accurate tracking performance has been achieved.

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.

Fuzzy controlled sensorless speed control of induction motor with MRAS based approach is proposed in this paper. Fuzzy logic controller is expected to be better controller as compared to traditional PI and PID controllers. The mathematical modeling of the MRAS module with inputs from induction motor stator voltages and currents taken as feedback to estimate the speed of the machine is described. The speed control of the induction motor is achieved by IRFOC without speed sensor at the motor. Induction motor speed and electromagnetic torque comparison with PI and FIS controllers integrated into the structure are represented by graphs plotted with time as a reference. Fuzzy controller is modeled with 49 rule base including two inputs and one output variable. All the modules design and the analysis is carried out in MATLAB software with ‘powergui’ toolbox for generating graphs.

Induction motors are one of the most common motors in use today because of their high robustness and low maintenance. To achieve high efficiency and maximum toque, controlling the speed of the induction motor becomes essential. This paper compares the performance of Proportional-Integral (PI) Controller, Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the speed and torque of a three-phase induction motor model. The effectiveness of each method is analyzed and presented with the help of simulation results. By comparing the effect in transient and state parameters, ANFIS shows higher efficiency and robustness than FLC and PI Controller for speed control.

Sliding mode control (SMC) of electric drives constitutes a very popular control method for nonlinear multivariable and time-varying systems, e.g., induction motor (IM) drives. Nowadays, IM are the most popular electrical machines (EM) applied in many industrial applications as motion control devices, including electrical and hybrid vehicles. Nowadays, the control systems of EM are mostly realized using digital techniques (microprocessors and microcontrollers). Therefore, all control algorithms should be discretized or the whole control system should be designed in the discrete-time domain. This paper deals with a discrete-time sliding mode control (DSMC) for IM drives. The discrete algorithms for sliding mode control of the motor speed and rotor flux are derived in detail and next tested in simulation research. The simulation tests include the discrete nature of the power converter supplying the IM and present excellent performance of the developed control structure. To obtain the rotor speed regulation invariant to external disturbances, like load torque or inertia, especially during the reaching phase of the switching line, the discrete version of a time-varying switching line was introduced. It is shown that the assumed dynamics of the IM flux and speed is achieved and the proposed control algorithm can be realized using commonly available microcontrollers. The paper is illustrated with comprehensive simulation results for 1.5 kW IM drive, which are verified by experimental tests.

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.

Induction motor is most commonly used drive in industrial applications. Many applications require precise speed control of induction motor drive with both steady state as well as dynamic state matching the desired pattern and in all cases immunity to disturbance is desired. In the past induction motors have been used for constant speed only but now due to availability of high performance, low loss semiconductor devices the speed control has become fast, accurate and efficient. Here in this study for more realistic results the voltage source SPWM inverter has been simulated with P-Spice and other blocks have been simulated in MATLAB-Simulink and both the aspects of speed set-point tracking control and regulatory control for load variations have been considered for an induction motor using closed loop control technique. Then using SLPS interfacing the results have been obtained for the two cases. The focus of this study is to consider the effect of characteristic of components used in the nonlinear final control element the inverter.

In this paper, the radial basis function neural network‐based model reference adaptive speed control for vector controlled induction motor drive system is presented. The speed control of induction motors is challenging because of their complex mathematical model, non‐linear structure, and time varying dynamics. The radial basis function neural network is used to compensate the non‐linearity which comes from the non‐linear state equations of induction motor model. Neural network parameters are online updated via gradient descent algorithm to minimize the error. The drive system has been tested under various operating conditions. This paper demonstrated benefits of the proposed control approach by comparing the algorithm to conventional PI controllers. The results show that the proposed controller ensures good robustness and stable operation of the system under variable speed and variable loads than the PI controller.