The Experts below are selected from a list of 6327 Experts worldwide ranked by ideXlab platform
B V Manikandan - One of the best experts on this subject based on the ideXlab platform.
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speed control of Brushless Dc Motor using bat algorithm optimized adaptive neuro fuzzy inference system
Soft Computing, 2015Co-Authors: K Premkumar, B V ManikandanAbstract:Bat algorithm optimized online ANFIS based speed controller presented for Brushless Dc Motor.The speed response of Brushless Dc Motor is analyzed for different operating conditions.The proposed controller eliminates the uncertainty problem due to load disturbance and set speed variations.The proposed controller enhances the time domain specifications and performance indices in all operating conditions. In this paper, speed control of Brushless Dc Motor using Bat algorithm optimized online Adaptive Neuro-Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (?), Forgetting Factor (λ) and Steepest Descent Momentum Constant (α) are optimized for different operating conditions of Brushless Dc Motor using Genetic Algorithm, Particle Swarm Optimization, and Bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm, Particle Swarm Optimization and Bat Algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless Dc Motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions.
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fuzzy pid supervised online anfis based speed controller for Brushless Dc Motor
Neurocomputing, 2015Co-Authors: K Premkumar, B V ManikandanAbstract:Abstract In this paper, two different speed controllers i.e., fuzzy online gain tuned anti wind up Proportional Integral and Derivative (PID) controller and fuzzy PID supervised online ANFIS controller for the speed control of Brushless Dc Motor have been proposed. The control system parameters such as rise time, settling time, peak time, recovery time, peak overshoot and undershoot of speed response of the Brushless Dc Motor with the proposed controllers have been compared with already published controllers such as anti wind up PID controller, fuzzy PID controller, offline ANFIS controller, PID supervised online ANFIS controller and On-line Recursive least square—error back propagation algorithm based ANFIS controller. In order to validate the effectiveness of the proposed controllers, the Brushless Dc Motor is operated under constant load condition, varying load conditions and varying set speed conditions. The simulation results under MATLAB environment have predicted better performance with fuzzy PID supervised online ANFIS controller under all operating conditions of the drive.
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adaptive neuro fuzzy inference system based speed controller for Brushless Dc Motor
Neurocomputing, 2014Co-Authors: K Premkumar, B V ManikandanAbstract:Abstract In this paper, a novel controller for Brushless Dc (BLDc) Motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the Motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the Brushless Dc Motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions.
Gang Liu - One of the best experts on this subject based on the ideXlab platform.
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precise accelerated torque control for small inductance Brushless Dc Motor
IEEE Transactions on Power Electronics, 2013Co-Authors: Jiancheng Fang, Xinxiu Zhou, Gang LiuAbstract:In this paper, precise accelerated torque control for a small inductance Brushless Dc Motor (BLDcM) is achieved by electromagnetic torque control and disturbance torque suppression. First, the electromagnetic torque ripple is reduced in commutation and conduction regions. In the former region, the ripple is suppressed by overlapping commutation control and optimizing the duty ratio of the active controller. In the latter region, the unbalance ripple caused by the unbalanced three phase windings is reduced by the proposed asymmetry compensation function, and the disturbance ripple created by the back electromotive force (EMF) is compensated by feedforward control. Second, the disturbance torque has been observed and compensated through the improved disturbance torque controller whose compensation coefficient is obtained by line-to-line back EMF coefficient estimation. And, both the disturbance observation and speed measurement are all synchronized with the encoder pulse alteration. Experimental results are presented to demonstrate the validity and effectiveness of the proposed accelerated torque control scheme.
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instantaneous torque control of small inductance Brushless Dc Motor
IEEE Transactions on Power Electronics, 2012Co-Authors: Jiancheng Fang, Xinxiu Zhou, Gang LiuAbstract:Due to the small inductance, modulation of three-phase inverter induces serious torque ripple. In addition, the increase in modulation frequency is limited by the processor speed. Therefore, the traditional torque control approaches are not suitable. In order to solve the aforementioned problems, a new instantaneous torque control method for small inductance Brushless Dc Motor is proposed by improving the torque estimation and control. First, instantaneous torque is estimated through improved position information and back electromotive force (EMF) coefficient estimation. The former is achieved by the proposed hall sensors position calibration and compensation method, and the latter is obtained by neural network fitting. Second, the torque ripple reduction is realized in the conduction and commutation region. The ripple caused by three-phase inverter modulation is suppressed by the Dc-link buck converter pulsewidth modulation control method. Upon this, an asymmetry compensation function is designed to solve the problem of unbalance among three phase windings. After that, back EMF disturbance, which is applied to current dynamics, is compensated through feedforward control. Subsequently, the commutation ripple is reduced by the outgoing phase control. Finally, the validity of the proposed torque control method is verified through experimental results.
Ick Choy - One of the best experts on this subject based on the ideXlab platform.
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commutation torque ripple reduction in Brushless Dc Motor drives using a single Dc current sensor
IEEE Transactions on Power Electronics, 2004Co-Authors: Joong-ho Song, Ick ChoyAbstract:This paper presents a comprehensive study on reducing commutation torque ripples generated in Brushless Dc Motor drives with only a single Dc-link current sensor provided. In such drives, commutation torque ripple suppression techniques that are practically effective in low speed as well as high speed regions are scarcely found. The commutation compensation technique proposed here is based on a strategy that the current slopes of the incoming and the outgoing phases during the commutation interval can be equalized by a proper duty-ratio control. Being directly linked with deadbeat current control scheme, the proposed control method accomplishes suppression of the spikes and dips superimposed on the current and torque responses during the commutation intervals of the inverter. Effectiveness of the proposed control method is verified through simulations and experiments.
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commutation torque ripple reduction in Brushless Dc Motor drives using a single Dc current sensor
Power Electronics Specialists Conference, 2002Co-Authors: Chang-hee Won, Joong-ho Song, Ick ChoyAbstract:This paper presents a comprehensive study result on reducing the commutation torque ripples generated in Brushless Dc Motor drives with only a single Dc-link current sensor provided in the inverter Dc-link. In Brushless Dc Motor drives equipped with only a single current sensor, it seems that commutation torque ripple suppression which is practically effective in low speed as well as high speed regions has not been reported. A proposed commutation compensation technique combined with a deadbeat Dc-link current controller is based on a strategy that the current slopes of the incoming and the outgoing phases during the commutation interval can be equalized by a proper duty-ratio control. The proposed control method is verified through simulations and experiments.
Jiancheng Fang - One of the best experts on this subject based on the ideXlab platform.
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precise accelerated torque control for small inductance Brushless Dc Motor
IEEE Transactions on Power Electronics, 2013Co-Authors: Jiancheng Fang, Xinxiu Zhou, Gang LiuAbstract:In this paper, precise accelerated torque control for a small inductance Brushless Dc Motor (BLDcM) is achieved by electromagnetic torque control and disturbance torque suppression. First, the electromagnetic torque ripple is reduced in commutation and conduction regions. In the former region, the ripple is suppressed by overlapping commutation control and optimizing the duty ratio of the active controller. In the latter region, the unbalance ripple caused by the unbalanced three phase windings is reduced by the proposed asymmetry compensation function, and the disturbance ripple created by the back electromotive force (EMF) is compensated by feedforward control. Second, the disturbance torque has been observed and compensated through the improved disturbance torque controller whose compensation coefficient is obtained by line-to-line back EMF coefficient estimation. And, both the disturbance observation and speed measurement are all synchronized with the encoder pulse alteration. Experimental results are presented to demonstrate the validity and effectiveness of the proposed accelerated torque control scheme.
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soft start control system for high speed Brushless Dc Motor
2012Co-Authors: Yanzhao He, Jiancheng Fang, Shi Hong, Rong Wu, Bangcheng Han, Shiqiang Zheng, Jinji SunAbstract:A soft start control system for a high-speed Brushless Dc Motor mainly comprises a digital controller, a controllable three-phase rectifier bridge, a Dc chopper, a three-phase inverter bridge, a current sensor, the high-speed Brushless Dc Motor and other parts. In order to solve the problem that the start current of the high-speed Brushless Dc Motor is large, the controllable three-phase rectifier bridge and the Dc chopper are adopted to carry out two-stage voltage modulation so as to control the Dc bus voltage of the three-phase inverter bridge; and the digital controller is adopted to regulate the duty ratio of five-path PWM (Pulse-Width Modulation) drive signals and six-path phase commutation signals according to three-phase inverter bridge Dc bus current feedback signals and Motor rotation speed feedback signals and control the three-phase inverter bridge Dc bus voltage and the phase commutation frequency so as to realize the voltage-reduction variable-frequency soft start of the Brushless Dc Motor. The soft start control system provided by the invention effectively solves the soft start problem of the high-speed Brushless Dc Motor, and has a significant application value for the research of the soft start control of the high-speed Brushless Dc Motor.
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instantaneous torque control of small inductance Brushless Dc Motor
IEEE Transactions on Power Electronics, 2012Co-Authors: Jiancheng Fang, Xinxiu Zhou, Gang LiuAbstract:Due to the small inductance, modulation of three-phase inverter induces serious torque ripple. In addition, the increase in modulation frequency is limited by the processor speed. Therefore, the traditional torque control approaches are not suitable. In order to solve the aforementioned problems, a new instantaneous torque control method for small inductance Brushless Dc Motor is proposed by improving the torque estimation and control. First, instantaneous torque is estimated through improved position information and back electromotive force (EMF) coefficient estimation. The former is achieved by the proposed hall sensors position calibration and compensation method, and the latter is obtained by neural network fitting. Second, the torque ripple reduction is realized in the conduction and commutation region. The ripple caused by three-phase inverter modulation is suppressed by the Dc-link buck converter pulsewidth modulation control method. Upon this, an asymmetry compensation function is designed to solve the problem of unbalance among three phase windings. After that, back EMF disturbance, which is applied to current dynamics, is compensated through feedforward control. Subsequently, the commutation ripple is reduced by the outgoing phase control. Finally, the validity of the proposed torque control method is verified through experimental results.
K Premkumar - One of the best experts on this subject based on the ideXlab platform.
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speed control of Brushless Dc Motor using bat algorithm optimized adaptive neuro fuzzy inference system
Soft Computing, 2015Co-Authors: K Premkumar, B V ManikandanAbstract:Bat algorithm optimized online ANFIS based speed controller presented for Brushless Dc Motor.The speed response of Brushless Dc Motor is analyzed for different operating conditions.The proposed controller eliminates the uncertainty problem due to load disturbance and set speed variations.The proposed controller enhances the time domain specifications and performance indices in all operating conditions. In this paper, speed control of Brushless Dc Motor using Bat algorithm optimized online Adaptive Neuro-Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (?), Forgetting Factor (λ) and Steepest Descent Momentum Constant (α) are optimized for different operating conditions of Brushless Dc Motor using Genetic Algorithm, Particle Swarm Optimization, and Bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm, Particle Swarm Optimization and Bat Algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless Dc Motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions.
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fuzzy pid supervised online anfis based speed controller for Brushless Dc Motor
Neurocomputing, 2015Co-Authors: K Premkumar, B V ManikandanAbstract:Abstract In this paper, two different speed controllers i.e., fuzzy online gain tuned anti wind up Proportional Integral and Derivative (PID) controller and fuzzy PID supervised online ANFIS controller for the speed control of Brushless Dc Motor have been proposed. The control system parameters such as rise time, settling time, peak time, recovery time, peak overshoot and undershoot of speed response of the Brushless Dc Motor with the proposed controllers have been compared with already published controllers such as anti wind up PID controller, fuzzy PID controller, offline ANFIS controller, PID supervised online ANFIS controller and On-line Recursive least square—error back propagation algorithm based ANFIS controller. In order to validate the effectiveness of the proposed controllers, the Brushless Dc Motor is operated under constant load condition, varying load conditions and varying set speed conditions. The simulation results under MATLAB environment have predicted better performance with fuzzy PID supervised online ANFIS controller under all operating conditions of the drive.
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adaptive neuro fuzzy inference system based speed controller for Brushless Dc Motor
Neurocomputing, 2014Co-Authors: K Premkumar, B V ManikandanAbstract:Abstract In this paper, a novel controller for Brushless Dc (BLDc) Motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the Motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the Brushless Dc Motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions.
