Control Of Linear Induction Motor Research Articles

Double-Closed-Loop Model Predictive Control Based on a Linear Induction Motor

The conventional PI control, which has been extensively employed in the high-performance control of linear induction motors, has the benefit of a straightforward concept, quick dynamic response, and simple control. Nevertheless, the linear induction motor is more susceptible to outside disturbances and mismatched parameters since it is a highly linked, high-order, nonlinear, time-varying, complex system. Because of this, this paper proposes a double-closed-loop control of the linear induction motor, whose speed and current loops are the model-predictive speed controller and the model-predictive current controller, respectively, to solve the problems of speed overshoot and oscillation when the linear induction motor is operated under PI control. The findings demonstrate the benefits of the suggested control strategy, which significantly enhances the linear induction motor’s speed control performance. These benefits include improved dynamic performance, limited overshooting, and robust resistance to load disturbance.

Sliding-Mode Control of Linear Induction Motor Based on Exponential Reaching Law

As a strongly coupled, multivariable, high-order nonlinear, time-varying complex system, a linear induction motor is susceptible to outside disturbances and mismatched parameters. Because of its straightforward control mechanism and fixed PI value, the traditional PI regulator is frequently utilized in linear induction motor speed control systems. However, because of these limitations, it frequently falls short of meeting the high-performance control needs of the motor in complex operating conditions. In order to solve the contradiction between the sliding-mode motion reaching time and vibration, the sliding-mode variable structure control is applied to the linear induction motor speed control system in this paper. The sliding-mode controller is designed using the exponential reaching law method, and the designed system is analyzed through simulation. The findings demonstrate how the controller enhances the system’s robustness and disturbance resistance by bringing about the advantages of rapidity, stability, no overshooting, and a strong resistance to load disturbance.

Speed Control of a Linear Induction Motor Using V/F Technique

Speed control of single phase Linear Induction Motor using V/F method have been widely used in fans and pumps drives. High-power Linear Induction Motors (LIMs) face challenges in managing speed. Traditional system are ineffective, causing inefficiency and potential damage. To address this, our project proposes a control system for LIM speed control suing V/f method. Currently, linear induction motors (LIMs) are widely used in many industrial applications, including transportation, conveyor systems, actuators, material handling, pumping of liquid metal, sliding-door closers and others, with satisfactory performance. The proposed control system consists of Arduino controller , soft switching for controlling and motor driver board to run Linear induction motor with voltage and frequency monitoring. This control system has almost no effect in normal operation of LIM. Hardware testing results are obtained to verify the valuable operation of proposed circuit in Speed control of LIM. Keywords—Linear Inuction Motor , Speed Control, V/f Method , Arduino Controller , LCD Display etc.

Direct electromagnetic force control of linear induction motor with end effects using fuzzy controller

In this article is a present dynamic model of a linen induction motor with end effects, occurs when the electromagnetic system of the motor is open. Instead of the angular speed and electromagnetic torque as a rotation motor, in the linear induction motor we have linear speed and linear electromagnetic force, which are sufficiently influenced by the open electromagnetic system of the primary and secondary elements of the linear motor. The choice of an appropriate control system for a linear induction motor is of particular importance considering the use of this type of motor in manufacturing. There are different types of control schemes for standard rotary motors that have their own advantages and disadvantages. When choosing a motor control scheme, we strive to achieve the following parameters, such as smoothness of regulation, ability to regulate in all four quadrants, accuracy of regulation, range of regulation and last but not least, simplicity of regulation. One such method that meets these criteria for rotary motors is the direct torque control method with space vector modulation. The control system of the linear induction motor proposed in this paper is based on this method, taking into account the open electromagnetic system and the occurrence of the end effects of the linear induction motor. Instead of the angular reference velocity as a rotary motor, in this case we have a linear reference velocity to the input, which is compared to the current linear velocity of the motor. The present paper also uses the vector modulation method by analogy with direct torque control with space vector modulation of the rotary motor. For the conversion of a linear velocity to a linear electromagnetic force, instead of a standard PI controller, it is using a fuzzy controller to convert a linear velocity to a reference electromagnetic force.

Research on sectional constant slip control of linear induction motor based on parameter self-tuning

Linear induction motor (LIM) has the advantages of low noise, strong climbing ability and small turning radius. It is widely used in middle-low speed maglev traffic. However, in actual operation, the linear motor will weaken the thrust output due to the existence of the end effect which will lead to the reduction of the traction efficiency. At the same time, the fluctuation of normal force will disturb the suspension system and affect its suspension stability. To solve the above problems, this paper first establishes a LIM dynamic model considering the end effect, and proposes constant slip frequency vector control. According to the different requirements of normal force and thrust of the train in constant power and force area, the sectional constant slip control is carried out. Then an improved particle swarm optimization (IPSO) is introduced to optimize slip frequency at two stages to reduce the theoretically solved error of optimal slip frequency. Finally, the proposed algorithm is simulated and hardware-in-the-loop tests are carried out; The experimental results show that the proposed algorithm and improvements can meet the requirements of full speed thrust output and normal force of the train. It provides a certain reference value for traction control of the linear motor in middle-low speed maglev train.

Flux-Weakening Control of Linear Induction Motor Drives Considering the Primary Resistance and End Effect

Flux-Weakening Control of Linear Induction Motor Drives Considering the Primary Resistance and End Effect

Motion control of linear induction motor using self-recurrent wavelet neural network trained by model predictive controller

Due to end effects phenomena that cause a decrease of air-gap flux and thrust force, obtaining a precise velocity for a linear induction motor (LIM) has become a significant challenge. This study suggests implementing a novel controller based on a self-recurrent wavelet neural network (SRWNN) and model predictive controller (MPC) to regulate the velocity and thrust force of LIM. The MPC was used to train the SRWNN in this study. The ultimate goal of employing such a control approach in neural network training is to reduce the degree of uncertainty caused by changes in motor parameters and load disturbance. The indirect field-oriented control (IFOC) approach was used to investigate velocity and flux control under varied loading circumstances. Furthermore, to supply the required LIM stator voltage, a SVPWM dependent voltage source inverter was used in this work. To ensure reliable performance, the suggested system combines the benefits of neural networks with the MPC method, resulting in a versatile controller with a basic construction that is easy to accomplish. The MATLAB package is utilized to simulates and outputs LIM responses. The results confirm that the proposed method, which efficiently controls the velocity and thrust force of the LIM, can cope with changes in load force disruption and motor parameters.

Adaptive backstepping control of linear induction motors using artificial neural network for load estimation

Linear induction motors (LIMs) make performing a direct linear motion possible without any mechanical rotary to linear motion transforming parts. <span lang="EN-US">Obtaining a precise mathematical model of such type of motors presents a difficulty due to time varying parameters and external load disturbance. This paper proposes an adaptive backstepping controller structure based on lyapunov stability for controlling a LIM position. Which can guarantee the annulment of position tracking error, despite of parameter uncertainties. Parameter update laws are extracted to estimate mover mass, friction coefficient and load force disturbance, which are assumed to be constant parameters; as a result, compensating their undesirable effect on control design. Then, load disturbance estimate is replaced with an artificial neural network (ANN) to reduce the estimation error. The numerical validation has shown better performance compared to the conventional backstepping controller, and proved the robustness of the proposed adaptive controller design against parameter changes.</span>

Sliding Mode Observer for Speed Sensorless Linear Induction Motor Drives

In this paper, a sliding mode observer (SMO) for the speed-sensorless direct torque vector control of linear induction motor(LIM) is proposed, considering the influence of dynamic end effects. The proposed SMO estimate the motor speed, the rotor flux, the angular position of the rotor flux, and the speed dependent parameter w, only by using the measured stator voltages and stator currents. The nonlinear sliding mode technique guarantees very good performance for different situations of motor speed. The proposed SMO does not use complicated observer gains and robust to the parameter variation over the full-speed range. Moreover, the convergence of SMO is proven by using the Lyapunov theory. Furthermore, LIM Drive state observability is presented with detailed analysis. The performance of the proposed SMO is verified by hardware-in-the-loop test (HIL) system. Furthermore, a comparative study with and Luenberger observer(LO) and model reference adaptive system(MRAS) is presented.

The Study on sensor-less vector control of linear induction motor

The Study on sensor-less vector control of linear induction motor

Adaptive Command-Filtered Fuzzy Nonsingular Terminal Sliding Mode Backstepping Control for Linear Induction Motor

This paper puts forward a projection-based adaptive command filtered fuzzy nonsingular terminal sliding mode backstepping (PACFTB) control method for the speed control of the linear induction motor (LIM) with unknown end effects. Firstly, the technique of fuzzy logic systems (FLS) is investigated to approximate the nonlinear components of the LIM’s mathematical model, which reduces the difficulty and cost of controller design. Then, a constrained command-filtered backstepping controller is designed with a filtering compensator compensating for the inherent error of constrained filter. Moreover, the nonsingular terminal sliding mode control method is combined in the controller design for its advantages of finite-time convergence of the system, and the projection-operator-based adaptive laws are established at the same time. Finally, the stability analysis proves that the boundedness and stability of all signals can be ensured with the proposed PACFTB controller, and the simulation results along with experiment results verify that the proposed control strategy has better control performance than the conventional command filter backstepping and PI controller.

Speed-Sensorless Control of Linear Induction Motor Based on the SSLKF-PLL Speed Estimation Scheme

In most motor drive applications, the speed sensor is of concern because it potentially deteriorates the system reliability and increases the system cost. To mitigate the adverse effects of speed sensors, various speed estimation schemes are employed to achieve speed-sensorless control of motor drives. With this, the concept of the steady-state linear Kalman filter-based phase-locked loop (SSLKF-PLL) is adopted in this article to enable speed estimation for speed-sensorless control of linear induction motor drives. However, the SSLKF-PLL scheme may be challenged by disturbances, e.g., harmonics and parameter variations in practice. To address this issue, the disturbance rejection schemes, including the prefilters and amplitude normalization unit, are developed for the SSLKF-PLL scheme in this article. Additionally, the dynamic performance analysis and parameter tuning of the SSLKF-PLL scheme are detailed by using the derived small-signal model. Experimental tests have validated the analysis and discussions of the SSLKF-PLL scheme.

Time-varying state observer based twisting control of linear induction motor considering dynamic end effects with unknown load torque

This paper presents a time-varying state observer based twisting control (TC) technique scheme for linear induction motor (LIM) considering dynamic end effects. With Ducan’s T-model, indirect field oriented control strategy is introduced and the state space equation of LIM is obtained in (d, q) rotary reference frame. On this basis, LIM’s model is extended to two subsystems: flux extended model and speed extended model. Twisting algorithm (TA) is applied into these two subsystems to achieve speed tracking and flux tracking with finite time convergence in the presence of disturbance and uncertainty. For the rotor flux estimation, a time-varying state observer is designed, and its stability and convergence have been proved with Lyapunov’s theory. Moreover, the value of estimated LIM speed derivative is obtained via second order sliding mode observer. Furthermore, a reduced order load torque observer is designed to estimate the external load torque. The feasibility and effectiveness of proposed method has been validated by Hardware-in-the-loop (HIL) test experiment.

The study on vector control of linear induction motor for sensorless

The study on vector control of linear induction motor for sensorless

Algorithm of Linear Induction Motor Control for Low Normal Force of Magnetic Levitation Train Propulsion System

This paper presents the analyses and experimental results of a single-sided linear induction motor (SLIM) thrust and normal force for the propulsion of semi-high-speed magnetic levitation (maglev) trains. These trains are composed of a levitation system that uses electromagnetic suspension and a propulsion system that uses SLIMs. The propulsion system of maglev trains using SLIMs has better low noise and dynamic characteristics compared with those using rotators. However, it has nonlinear characteristics due to the effect of slip that occur in the secondary eddy-current induction process; the normal force generated by the SLIM can negatively affect the levitation control. Therefore, a new slip-control algorithm is proposed for the safe operation of maglev trains, reflecting the normal force of the motor in propulsion control. First, the SLIM thrust and normal force are analyzed through the finite-element method (FEM) for a precise analysis of the slips. Furthermore, a slip range with a low normal force was derived based on the FEM analysis results, and these results are reflected in the propulsion control algorithm. Finally, the new algorithm was validated by an application to a full-sized testing apparatus.

Adaptive command-filtered fuzzy backstepping control for linear induction motor with unknown end effect

In this paper, the problem of the speed control is considered for linear induction motor with the end effects, uncertain system parameters, and external load disturbance. Firstly, the proposed controller is based on backstepping method, and the adaptive laws are designed.The fuzzy logic modeling approach is used to deal with the unknown nonlinear functions and uncertain parameters. Secondly, a constrained command filter is employed to overcome the problem of differential expansion and controller saturation in the backstepping method, and a filter’s compensating signal is used to mitigate the error caused by the constrained command filter. Thirdly, the projection operator introduced in the adaptive laws guarantees boundedness of the estimated functions and robustness of the designed controller in the presence of time-varying disturbances and uncertainties. Thus, the proposed control controller can ensure that all response signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified by simulation and comparison with command-filtered backstepping controller.

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

This paper contains a proposed controller based on optimal recurrent wavelet neural network (RWNN) with PID controller to control the velocity and hence the stator current as well as the developed thrust of three phase linear induction motor (LIM) which consider the end effects. A vector control represented by indirect field oriented control (IFOC) technique is appointed to attain velocity and flux control for different loading conditions. Moreover, a voltage source inverter based on space vector pulse width modulation (SVPWM) is utilized to give the required stator voltage of LIM. A well-known particle swarm optimization (PSO) algorithm is employed for online tuning of the proposed controller. The computer simulation results show that this controller is effective and gives preferable and rigorous performance compared with a results obtained from conventional wavelet neural network (WNN) and PID controllers.

Loss Minimization Control of Linear Induction Motor Drive for Linear Metros

The linear induction motor (LIM) drive in linear metros suffers heavily from low efficiency due to its large air-gap length and the partial load conditions, where high loss appears in both the LIM and the inverter when a constant excitation current is generally required. Worse still, the end-effects, including both the transversal edge-effect and longitudinal end-effect, would lead to the decrease of magnetizing inductance and the increase of secondary resistance, resulting in extra loss and further deterioration of drive efficiency. To reduce the loss of the LIM drive, this paper proposes a novel loss model based loss minimization control (LMC) scheme for LIM drives. First, in the equivalent circuit and the mathematical model of LIM, four coefficients are introduced to evaluate the influence of the end-effects. Based on a thorough analysis of the LIM copper and core losses, together with the inverter conduction and switching losses, a novel integrated loss model of LIM drive is then developed, and an improved LMC scheme to obtain the optimal solution online is proposed to minimize the loss of the LIM drive. The proposed control method is successfully implemented in a 3-kW LIM drive prototype. The effectiveness of the proposed method is validated by the experimental results.

Adaptive Robust Motion Control for Linear Induction Motor with Electromagnetic Nonlinearity Compensation

AbstractThis paper presents an adaptive robust motion control algorithm for linear induction motor (LIM) with electromagnetic nonlinearity compensation to suppress nonlinear magnetic thrust reduction due to the end effect. The electromagnetic compensation adaptive robust control (ECARC) algorithm is developed to deal with the parametric uncertainties and uncertain nonlinearities that are widely present in LIM. This algorithm employs a discontinuous projection‐type method to estimate the system parameters. The disturbance is bounded by a critical value, which guarantees that the system is exponentially stable. However, due to the magnetic field distortion caused by the opening of primary ends, the relationship between the driving current and the resultant motor thrust is nonlinear. Consequently, electromagnetic thrust and flux are calculated and used for feedback compensation. Different from the traditional vector control and direct torque control, the control algorithm employs electromagnetic thrust and magnetic flux to compensate torque current and magnetic field. Simulation results and comparative experimental results show that current control algorithm is accurate and efficient.

Sliding‐mode observer‐based speed‐sensorless vector control of linear induction motor with a parallel secondary resistance online identification

This study proposes a speed estimation scheme for the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low-speed maglev applications, which is composed of two parts: (i) a sliding mode model reference adaptive system observer for speed estimation; and (ii) a parallel secondary resistance online identification for achieving the improvements of the proposed speed estimation scheme performance. The sliding mode observer (SMO) is established on the basis of the state space-vector model of the LIM considering the dynamic end effect. Based on SMO, both speed and secondary resistance estimation algorithms are obtained by utilising Popov's hyperstability theory. Moreover, the Lyapunov stability theory is adopted for the stability analysis of the proposed speed estimation scheme. The effectiveness of the proposed speed estimation algorithm has been verified and compared with the performance of the conventional speed estimation scheme based on single-manifold SMO by the simulation and hardware-in-the-loop tests.