Position Control Of Induction Motor Research Articles

Fast terminal sliding mode control design for position control of induction motors using adaptive quantum neural networks

In this work, an efficient robust control scheme is proposed for position control of induction motors based on field-oriented control (FOC). The proposed control scheme consists of three interior loops. In the outer loop, the central controller based on the nonsingular fast terminal sliding mode control (NFTSM) is designed. The objective of this loop is to provide the required stator current in the q-axis for regulating the electromagnetic torque. In the inner loops, two fractional-order PI (FOPI) controllers are designed to provide the stator voltages. Besides, since induction motors have a complex mathematical model, time-varying dynamics, and non-linear structure, to deal with these challenges in the controller design, the quantum neural network (QNN) is presented with a novel framework and employed to estimate the lumped uncertainties. This makes the main contribution of the paper. In addition, to further improve system performance, the parameters of the FOPI controllers are tuned by employing the artificial bee colony (ABC) optimization algorithm. The stability of the proposed control approach is proved by Lyapunov’s stability theory. According to the results of the comparative simulation, the superiority of this approach is confirmed.

Dynamic sliding mode position control of induction motors based load torque compensation using adaptive state observer

PurposeThe purpose of this paper is position control scheme for a servo induction motor (SIM) with uncertainty has been designed using a new observer issue and a dynamic sliding mode control (DSMC).Design/methodology/approachIn DSMC, the chattering is removed due to the integrator (or a low-pass filter) which is placed before the input control of the plant. However, in DSMC, the augmented system has one dimension bigger than the actual system (if integrator is used) and then, the plant model should be completely known. To solve this problem in SIM, the use of a new adaptive state observer (ASO) is proposed.FindingsThe advantage of the proposed approach is to maintain the system controlled under the external load torque variations. Then, the load variations do not affect the motor positioning. Moreover, it is demonstrated that the observer error converges to zero based on the Lyapunov stability theory.Originality/valueThe knowledge of the upper bound for the system uncertainty is not necessary in an adaptive state observer, which is important in practical implementation. Simulation results are presented to demonstrate the performance of the proposed approach.

Generalized predictive control robust for position control of induction motor using field-oriented control

This paper presents the study and implementation of a different field-oriented control strategy using a generalized predictive control (GPC) technique applied to the mechanic position loop aiming to obtain a system that acts in the fractional horsepower motor driver running at near zero frequency. The position and speed loops were identified to verify the system behavior and, from the model found design the GPC controller. Simulation and experimental results are shown and discussed to demonstrate the merit of the proposed approach and the performance and robustness of the algorithms have been evaluated.

Particle Swarm Optimization for Position Control of Induction Motor

This paper proposes a position control of induction motor using particle swarm optimization (PSO) and fuzzy phase plane controller. Fuzzy membership functions, phase plane theory and the PSO are employed to design the proposed controller (FPPC) for controlling the position of an induction motor, based on the desired specifications. The proposed FPPC has merits of rapid response, simply designed fuzzy logic control and an explicitly designed phase plane theory. Simulations and experimental results reveal that the proposed FPPC is superior in optimal position control to conventional PI controller.

Performance analysis of neuro-fuzzy position controller for vector controlled induction motor drive

This paper presents the modelling, simulation, and performance analysis of a hybrid neuro–fuzzy logic controller for position control of induction motor using vector control techniques. In this paper, the space vector pulse width modulated voltage source inverter fed induction motor drive has been considered. The adaptive neuro–fuzzy inference system (ANFIS) method is used to design the direct torque neuro–fuzzy controller (DTNFC). The ANFIS structure can be tuned automatically by least square estimation and back propagation algorithm. The MATLAB–SIMULINK models are developed for both cases of without and with neuro–fuzzy controller to simulate the position control scheme. The simulation results justify the comparative advantages of ANFIS based neuro–fuzzy position controller which achieves the desired performance.

Adaptive PC-based backstepping position control of induction motor

An adaptive position tracking control scheme for induction motors is proposed subject to unknown load torque via adaptive backstepping design. An observer derived from the dynamical model is also given to provide the information of rotor flux angle which decides the proportion of input voltage in d-q frame. Besides, the controller is developed under a special non-linear coordinate transform such that position control objective can be fulfilled with backstepping approach. The underlying design concept is to endow the closed-loop system while lacking the knowledge of some mechanical system parameters, such as the motor inertia motor damping coefficient, and the unknown payload. The proposed control scheme comes along with a thorough proof based on Lyapunov stability theory. PC-based experimental results are also given to validate the effectiveness of the proposed control scheme.

A new Technique for Position Control of Induction Motor Using Adaptive Inverse Control

Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.

A new Technique for Position Control of Induction Motor Using Adaptive Inverse Control

Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.

Improved Discrete-Time Sliding-Mode Position Control Using Euler Velocity Estimation

This paper presents a design of digitally controlled positional systems with Euler velocity estimation within the framework of the discrete-time sliding mode (DSM). The effects of quantization and simple velocity estimation on DSM quality are analyzed. It is shown that the introduced and amplified quantization noise degrades sliding motion into the quasi-sliding mode and threatens to provoke chattering. Furthermore, a new DSM control algorithm is proposed, featuring a two-scale reaching law and a supplemental integral action. This algorithm avoids chattering and provides excellent performance. The developed DSM controller has been experimentally tested in an induction motor position control.

Adaptive backstepping controller for linear induction motor position control

PurposeThe purpose of this paper is to propose mover position control of linear induction motor (LIM) using an adaptive backstepping approach based on field orientation.Design/methodology/approachFirst, the indirect field‐oriented control LIM is derived. Then, an adaptive backstepping approach based on field‐oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Mover position amplitude tracking objective is formulated, under the assumption of unknown total mass of the moving element, viscous friction, and load force, so that the position regulation is achieved.FindingsThe effectiveness and robustness of the proposed control scheme are verified by numerical simulation using Matlab/Simulink model. The numerical validation results of the proposed scheme have presented good transient control performances and robustness to uncertainties compared to the conventional backstepping control design.Originality/valueThe paper presents an adaptive backstepping approach for LIM control that achieves mover position amplitude tracking objective under mechanical parameter variation.

A Passivity-Based Approach to the Rotor Resistance Estimation and Sliding Mode Control for Induction Motors

Based on the passivity theorem, an approach of the rotor resistance estimation and sliding mode position control for induction motor drives is proposed in this paper. Owing to values of the moment of inertia and the viscous coefficient of induction motors are generally small, the load torque disturbance is a critical term to create the chattering phenomenon. The adaptive laws are taken into account for estimating the rotor resistance and the gain of a load torque. Therefore, the chattering associated with the conventional sliding mode controller can be alleviated. The stability analysis of the estimator and the position control system is carried out by employing the passivity theorem. Finally, experimental results are presented to show the performance and robustness of the estimator and the controller with the variations of the motor mechanical parameters, rotor resistance, and load torque disturbances.

Adaptive Parameter Estimation and Position Control of Induction Motors Based on Passivity Theorem

This paper presents the passivity-based rotor resistance and mechanical paramters estimation, and the position control for induction motors. Firstly, the input-output linearization theory is employed to decouple the rotor flux amplitude and the rotor position at the transient state. An open-loop current model flux observer then estimates the rotor flux. Furthermore, we adopted the gradient algorithm to design adaptive laws to estimate the rotor resistance, moment of inertia, viscous coefficient, and load torque. The passive properties of the feedback connection of the rotor flux observer to the rotor resistance estimator, and the position controller are analyzed by the passivity theorem. According to the properties, the overall control system is proved to be globally stable without using Lyapunov-type arguments. Finally, experimental results are provided to show that the proposed method is robust to variations of the mechanical parameters and load torque disturbances. Moreover, good position tracking response and parameters estimating characteristic can be obtained.

Fuzzy neural network IP controller for robust position control of induction motor drive

In this paper, a fuzzy neural network (FNN) controller which emulates the conventional IP controller is proposed for a vector controlled induction motor drive. A Sugeno type FNN is adopted for the proposed control system and the fuzzy neural network is so designed that the FNN controller behaves an robust-nonlinear IP controller. The proposed FNN-IP controller is used for the position control of induction motor drive and the performance and the robustness of the control system is tested for nonlinear motor loads and parameter variations. The FNN-IP controller is trained off-line using experimental data’s and then the trained controller is used for experimental studies. DS1104 digital signal processor control card is used to implement the control algorithm. Experimental results showing the effectiveness of the proposed control system are presented for parameter and load variations of the motor.

Sensorless Position and Speed Control of Induction Motors Using High-Frequency Injection and Without Offline Precommissioning

This paper addresses the sensorless speed and position control of induction motors using high-frequency injection at zero and low frequencies. A novel algorithm is presented which allows the rejection of saturation and nonlinear inverter effects without the need for an offline precommissioning process. The method is based on a set of synchronous filters to identify the disturbance waveforms and a memory algorithm that refines the quality of the disturbance waveforms as the motor's operational history is increased. The algorithm is entirely sensorless. Experimental results show sensorless low-frequency operation with and without the memory algorithm.

DSP2812 마이크로프로세서를 이용한 CAN기반 지능형 복수전동기 제어시스템개발

This paper addresses the CAN based networked intelligent multi-motor control system using DSP2812 microprocessor. CAN built in DSP2812 microprocessor is used to control and monitor the multi-motor system with the inverter driving system CAN network implementation schemes and the algorithm for multi-motor control and monitoring is also developed. We configure the multi-motor control experimental system to verify the proposed algerian and the reliability of CAN networks system in the various operation of two induction motors. The experimental results show that CAN based networked intelligent multi-motor control system using DSP2812 microprocessor can carry out the real-time network based control in various speed range and the position control of induction motors.

An EKF-Based Estimator for the Speed Sensorless Vector Control of Induction Motors

This article offers a solution to the performance deteriorating effect of uncertainties in the sensorless control of induction motors (IMs). The major contribution of the study is the development and implementation of an extended Kalman filter (EKF) algorithm that takes electrical and mechanical uncertainties into account. In this regard, this is the first known study to estimate the mechanical uncertainties together with the rotor resistance, R′ r , without injecting high frequency signals. The EKF algorithm also estimates the rotor flux, angular velocity and stator currents with no apriori knowledge on the states and initial values taken as zero. Experiments performed under unknown load torque and with rotor resistance variations up to twice the rated value demonstrate the good performance and robustness of the estimation method. The algorithm also estimates the mechanical uncertainties as a constant state to capture the unknown viscous and Coulomb friction in steady-state; therefore, it could be used to improve the performance of the velocity or position control of IMs, if utilized in combination with a compensation scheme.

Passivity-Based Sliding Mode Position Control for Induction Motor Drives

In this paper, a passivity-based sliding-mode controller is proposed to control the motion of an induction motor. At first, the induction motor is proved to be a state strictly passive system. Then, a sliding-mode position controller with an adaptive load torque estimator is designed to control the position of the induction motor such that the chattering effects associated with a classical sliding-mode position controller can be eliminated. The stability analysis of the overall position control system is carried out by the passivity theory. The proposed approach is robust with regard to variations of motor mechanical parameters and load torque disturbances. Finally, experimental results are included to demonstrate that good position tracking can be obtained without the rotor flux observer.

Position control of induction motor a new‐bounded fuzzy sliding mode controller

PurposeThe aims of the paper are to improve the dynamic response of an induction motor based position servo system and to remove the chattering problem in the sliding mode control theory by using fuzzy logic principles. The obtained results are also compared with conventional sliding mode controller to show its performance.Design/methodology/approachThe main method used for the research is to form a thin boundary layer neighboring the switching surface by using fuzzy logic. The sliding mode control law is inherently discontinuous naturally. Therefore, there are some difficulties such as so many switches occurring between the control bounds, which cannot be carried out by real controllers. Therefore, fuzzy logic is used in the thin boundary layer to determine the control signal current. Thus, the chattering is eliminated.FindingsThe results show that the designed controller has superior performance. But, there are also some difficulties. It is difficult to obtain fuzzy rules. The rules can be obtained by using genetic algorithms without expert's knowledge. However, sliding surface slope C can be optimized to increase system's dynamic performance.Originality/valueA new boundary layer consisting of the fuzzy rules in the sliding mode control is formed.

Acquisition of Rotor Anisotropy Signals in Sensorless Position Control Systems

Sensorless position control of induction motors relies on the detection of rotor anisotropies. The repetitive transient excitation through the switched voltages of the pulsewidth-modulated inverter provides the test signals to identify the spatial orientation of the anisotropies that indicate the rotor position. The position information is contained in the inverter output voltages. These are measured against the neutral point of the star connected stator winding. The signals are heavily corrupted by disturbances. These originate from the propagation of traveling waves on a long motor cable and from the influence of high-frequency common-mode currents. The paper describes how a clean position signal is extracted in the presence of such noise.

Fuzzy Logic Controller based Position Control using Space Vector Pulse Width Modulation Voltage Source Inverter Fed Induction Motor Drives

DC permanent magnet motors are extensively used in position control system. The dynamics of induction motor using vector control are equivalent to that of PWM-controlled dc servo drives. A novel fuzzy logic controller based position control of induction motor using vector control techniques is presented in this paper. Here, the switching patterns of devices are generated directly by fuzzy logic. Simulation results show that the proposed control system recovers steady state, and damps out transient error quickly.