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Dr. Premkumar K
Rajalakshmi Engineering College

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0 Optimization
0 Soft Computing
0 Power Electronic Drives
0 power system
0 Electrical Engineering

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Journal article
Published: 10 April 2021 in Solar Energy
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In this paper, PV emulator is developed using double loop PI controlled DC-DC buck converter. Ambient environmental conditions such as irradiance, temperature and wind speed are considered for creating PV reference model in the PV emulator. The simulation model of the proposed PV emulator is created and tested using MATLAB Simulink package. To validate the effectiveness of the proposed PV emulator, it is tested with different operating conditions such as varying irradiance, temperature and wind speed. The current–voltage and power-voltage characteristic of the PV emulator is compared with PV reference model. The suitability of the PV emulator is tested with MPPT algorithm and battery charging controller in addition to number of objectives. Real time testing of the proposed PV emulator is done experimentally and also corresponding results are discussed. The proposed Solar PV emulator has the following properties: (1) higher bandwidth DC-DC buck converter, (2) extremely reliable performance with lower response time, and (3) lower output voltage and current ripple.

ACS Style

A. Nazar Ali; K. Premkumar; M. Vishnupriya; B.V. Manikandan; T. Thamizhselvan. Design and development of realistic PV emulator adaptable to the maximum power point tracking algorithm and battery charging controller. Solar Energy 2021, 220, 473 -490.

AMA Style

A. Nazar Ali, K. Premkumar, M. Vishnupriya, B.V. Manikandan, T. Thamizhselvan. Design and development of realistic PV emulator adaptable to the maximum power point tracking algorithm and battery charging controller. Solar Energy. 2021; 220 ():473-490.

Chicago/Turabian Style

A. Nazar Ali; K. Premkumar; M. Vishnupriya; B.V. Manikandan; T. Thamizhselvan. 2021. "Design and development of realistic PV emulator adaptable to the maximum power point tracking algorithm and battery charging controller." Solar Energy 220, no. : 473-490.

Research article
Published: 17 December 2020 in International Transactions on Electrical Energy Systems
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In this article, particle swarm optimized proportional integral (PI) controlled DC‐DC buck converter‐based proton‐exchange membrane fuel cell emulator has been proposed to test the maximum power point tracking algorithm and battery charging controller. Particle swarm optimization is used to optimize the proportional gain (Kp) and integral gain (Ki) of the proportional integral controller for emulating the characteristics of the PEM fuel cell. The simulation model of the proposed PEM fuel cell emulator is created and tested using MATLAB Simulink package. To validate the effectiveness of the proposed PEM fuel cell emulator, it is tested under different operating conditions. The polarization characteristics of the PEM fuel cell emulator are compared with PEM fuel cell reference model. The suitability of the PEM fuel cell emulator has been tested with MPPT algorithm and MPPT battery charging controller. Also, real‐time testing of the proposed PEM fuel cell emulator has been realized through experimental set up and the corresponding results are analyzed.

ACS Style

K. Premkumar; M. Vishnupriya; T. Thamizhselvan; P. Sanjeevikumar; B.V. Manikandan. PSO optimized PI controlled DC‐DC buck converter‐based proton‐exchange membrane fuel cell emulator for testing of MPPT algorithm and battery charger controller. International Transactions on Electrical Energy Systems 2020, 31, 1 .

AMA Style

K. Premkumar, M. Vishnupriya, T. Thamizhselvan, P. Sanjeevikumar, B.V. Manikandan. PSO optimized PI controlled DC‐DC buck converter‐based proton‐exchange membrane fuel cell emulator for testing of MPPT algorithm and battery charger controller. International Transactions on Electrical Energy Systems. 2020; 31 (2):1.

Chicago/Turabian Style

K. Premkumar; M. Vishnupriya; T. Thamizhselvan; P. Sanjeevikumar; B.V. Manikandan. 2020. "PSO optimized PI controlled DC‐DC buck converter‐based proton‐exchange membrane fuel cell emulator for testing of MPPT algorithm and battery charger controller." International Transactions on Electrical Energy Systems 31, no. 2: 1.

Journal article
Published: 11 December 2020 in Sustainability
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In this article, the parameters of the proportional-integral (PI) controller of the wind turbine (WT) emulator, i.e., proportional and integral gain of the PI controller, are optimized using a black widow optimization algorithm (BWOA). The proposed system is developed and analyzed using MATLAB/Simulink environment. The performance of the BWOA optimized PI controller is compared with a BAT algorithm, particle swarm optimization, and genetic algorithm optimized PI controller to measure the effectiveness of the proposed control system. The developed system is tested for different operating conditions such as static wind speed settings, static pitch angle conditions, step-change in wind speed settings, and step-change in pitch angle settings. Finally, the proposed system is realized in real-time by hardware experimentations. The results of the experimentation are compared with simulation results as well. The presented simulation and hardware result shows good agreement, which confirms the effectiveness of the proposed method. Thereby, the proposed optimization-based PI-controlled wind emulator can be recommended for emulating the characteristics of any type of WT with a low-cost system.

ACS Style

K. Premkumar; M. Vishnupriya; Thanikanti Sudhakar Babu; B. Manikandan; T. Thamizhselvan; A. Nazar Ali; Rabiul Islam; Abbas Kouzani; M. Parvez Mahmud. Black Widow Optimization-Based Optimal PI-Controlled Wind Turbine Emulator. Sustainability 2020, 12, 10357 .

AMA Style

K. Premkumar, M. Vishnupriya, Thanikanti Sudhakar Babu, B. Manikandan, T. Thamizhselvan, A. Nazar Ali, Rabiul Islam, Abbas Kouzani, M. Parvez Mahmud. Black Widow Optimization-Based Optimal PI-Controlled Wind Turbine Emulator. Sustainability. 2020; 12 (24):10357.

Chicago/Turabian Style

K. Premkumar; M. Vishnupriya; Thanikanti Sudhakar Babu; B. Manikandan; T. Thamizhselvan; A. Nazar Ali; Rabiul Islam; Abbas Kouzani; M. Parvez Mahmud. 2020. "Black Widow Optimization-Based Optimal PI-Controlled Wind Turbine Emulator." Sustainability 12, no. 24: 10357.

Journal article
Published: 08 August 2020 in Journal of Circuits, Systems and Computers
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This paper presents the solar powered charging control of lithium-ion battery. The flyback converter is used to extract the maximum power from the solar photovoltaic (PV) array and charge the battery. This paper also presents the fuzzy logic-based battery management system to protect the batteries due to overcharging and over-discharging conditions. The proposed method is designed and developed in the MATLAB/Simulink platform. Solar PV powered battery system is tested for step change in irradiance conditions and corresponding results are measured and analyzed. The effectiveness of the fuzzy logic-based battery management system is also presented. The simulation model for BMS technique has overall efficiency of 95.1%. In order to verify the effectiveness of the proposed system, experimental verification of the proposed method is implemented in real time and compared with simulation results.

ACS Style

P. Justin Raj; V. Vasan Prabhu; K. Premkumar. Fuzzy Logic-based Battery Management System for Solar-Powered Li-Ion Battery in Electric Vehicle Applications. Journal of Circuits, Systems and Computers 2020, 1 .

AMA Style

P. Justin Raj, V. Vasan Prabhu, K. Premkumar. Fuzzy Logic-based Battery Management System for Solar-Powered Li-Ion Battery in Electric Vehicle Applications. Journal of Circuits, Systems and Computers. 2020; ():1.

Chicago/Turabian Style

P. Justin Raj; V. Vasan Prabhu; K. Premkumar. 2020. "Fuzzy Logic-based Battery Management System for Solar-Powered Li-Ion Battery in Electric Vehicle Applications." Journal of Circuits, Systems and Computers , no. : 1.

Original paper
Published: 25 November 2019 in Electrical Engineering
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In this paper, adaptive neuro-fuzzy inference system and proportional integral controller-based maximum power point tracking algorithm are presented for solar powered brushless DC motor for water pumping application. Adaptive neuro-fuzzy inference with PI controller provides control gain to maximum power point tracker. It adjusts the duty cycle of the zeta converter for extracting maximum power from solar PV array. The performance of proposed controller is compared with the conventional perturb and observe method, fuzzy perturb and observe method and incremental conductance method. Simulation studies are carried out in MATLAB. The experimental verification is shown to prove the suitability and feasibility of the proposed controller. The results reveal that the adaptive fuzzy inference system with PI controller quickly tracks maximum power from solar PV array under different irradiance.

ACS Style

A. Alice Hepzibah; K. Premkumar. ANFIS current–voltage controlled MPPT algorithm for solar powered brushless DC motor based water pump. Electrical Engineering 2019, 102, 421 -435.

AMA Style

A. Alice Hepzibah, K. Premkumar. ANFIS current–voltage controlled MPPT algorithm for solar powered brushless DC motor based water pump. Electrical Engineering. 2019; 102 (1):421-435.

Chicago/Turabian Style

A. Alice Hepzibah; K. Premkumar. 2019. "ANFIS current–voltage controlled MPPT algorithm for solar powered brushless DC motor based water pump." Electrical Engineering 102, no. 1: 421-435.

Journal article
Published: 15 August 2018 in Current Signal Transduction Therapy
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In this paper, stability and performance analysis of adaptive neuro fuzzy inference system (ANFIS) tuned proportional integral derivative (PID) speed controller has been presented for brushless dc motor. The stability of the proposed controller is examined using Lyapunov and Nyquist stability criterion. The performance of the proposed controller is analyzed for step change in speed input and sudden load disturbance conditions and also compared with fuzzy PID, super twisting sliding mode control, and PID controllers. Sensitivity of the proposed controller is analyzed with parameter variation in inertia, permanent magnet flux, resistance and inductance of the motor. From the results, it is evident that the proposed controller is more stable and outperforms the other considered controllers in all performance aspects.

ACS Style

K. Premkumar; B.V. Manikandan. Stability and Performance Analysis of ANFIS Tuned PID Based Speed Controller for Brushless DC Motor. Current Signal Transduction Therapy 2018, 13, 19 -30.

AMA Style

K. Premkumar, B.V. Manikandan. Stability and Performance Analysis of ANFIS Tuned PID Based Speed Controller for Brushless DC Motor. Current Signal Transduction Therapy. 2018; 13 (1):19-30.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2018. "Stability and Performance Analysis of ANFIS Tuned PID Based Speed Controller for Brushless DC Motor." Current Signal Transduction Therapy 13, no. 1: 19-30.

Journal article
Published: 14 December 2017 in Electric Power Components and Systems
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In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (Ni) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.

ACS Style

Kamaraj Premkumar; Bairavan Veerayan Manikandan; Chellappan Agees Kumar. Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor. Electric Power Components and Systems 2017, 45, 2304 -2317.

AMA Style

Kamaraj Premkumar, Bairavan Veerayan Manikandan, Chellappan Agees Kumar. Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor. Electric Power Components and Systems. 2017; 45 (20):2304-2317.

Chicago/Turabian Style

Kamaraj Premkumar; Bairavan Veerayan Manikandan; Chellappan Agees Kumar. 2017. "Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor." Electric Power Components and Systems 45, no. 20: 2304-2317.

Original article
Published: 10 November 2016 in Neural Computing and Applications
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In this paper, a novel adaptive neuro-fuzzy inference system (ANFIS)-based control technique optimized by Bacterial Foraging Optimization Algorithm for speed control of matrix converter (MC)-fed brushless direct current (BLDC) motor is presented. ANFIS is considered to be one of the most promising technologies for control of electrical drives fed by MC. Optimizing the training parameters of ANFIS, to improve its performance, is still being considered by several researchers recently. Parameters of the online ANFIS controller such as learning rate (η), forgetting factor (λ) and steepest descent momentum constant (α) are optimized by using the proposed algorithm. For the purpose of comparison, proportional integral derivative controller, fuzzy logic controller, PSO-ANFIS and BAT-ANFIS are considered. Set point tracking performances of the proposed system are carried out at various operating points for an industrial BLDC motor operating at a maximum rated speed of 380 rpm and torque of 6.4 N m. Time domain specifications such as rise time, settling time, peak time, steady-state error and peak overshoot in the presence and absence of load torque disturbances are presented. Time integral performance measures such as integral square error, integral absolute error, and integral time multiplied absolute error are analyzed for various operating conditions. Speed fluctuation in the output of BLDC motor is dependent on the source current harmonics of the inverter/converter. To illustrate this, total harmonic distortion (THD) analysis is carried out for the existing PWM inverter and the proposed MC, and it is proved that MC results in reduced THD, as compared to PWM inverter. Simulation results confirm that the proposed controller outperforms the other existing control techniques under various set speed and torque conditions. Statistical analysis is effectively carried out to prove the effectiveness of the proposed controller. Experimental analysis is performed to validate the performance of the proposed control scheme.

ACS Style

T. S. Sivarani; S. Joseph Jawhar; C. Agees Kumar; K. Premkumar. Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque. Neural Computing and Applications 2016, 29, 1411 -1434.

AMA Style

T. S. Sivarani, S. Joseph Jawhar, C. Agees Kumar, K. Premkumar. Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque. Neural Computing and Applications. 2016; 29 (12):1411-1434.

Chicago/Turabian Style

T. S. Sivarani; S. Joseph Jawhar; C. Agees Kumar; K. Premkumar. 2016. "Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque." Neural Computing and Applications 29, no. 12: 1411-1434.

Research articles
Published: 01 September 2016 in IET Power Electronics
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In this study, fuzzy supervised online coactive neuro-fuzzy inference system (CANFIS)-based rotor position controller is presented for brushless DC (BLDC) motor. An online learning algorithm is employed for updating premises and consequent parameters of the CANFIS controller. The rotor position control of BLDC motor is simulated using MATLAB/Simulink Toolbox. The dynamic response of the BLDC motor with proposed controller is measured for standard sinusoidal reference input. The effectiveness of the proposed controller performance is compared with fuzzy proportional–integral derivative controller, adaptive neuro-fuzzy inference system controller and supervised recurrent fuzzy neural network controller. The proposed controller is able to solve the problem of non-linearities and uncertainty due to reference input changes of BLDC motor and guarantees fast and accurate dynamic response to a remarkable steady-state performance. Also, experimental hardware results are presented to demonstrate the validity and effectiveness of the proposed control scheme using field programmable gate array chip. Experimental results show that the proposed control scheme can achieve a more favourable tracking performance without the chattering phenomena in the control effort.

ACS Style

M. John Prabu; P. Poongodi; K. Premkumar. Fuzzy supervised online coactive neuro‐fuzzy inference system‐based rotor position control of brushless DC motor. IET Power Electronics 2016, 9, 2229 -2239.

AMA Style

M. John Prabu, P. Poongodi, K. Premkumar. Fuzzy supervised online coactive neuro‐fuzzy inference system‐based rotor position control of brushless DC motor. IET Power Electronics. 2016; 9 (11):2229-2239.

Chicago/Turabian Style

M. John Prabu; P. Poongodi; K. Premkumar. 2016. "Fuzzy supervised online coactive neuro‐fuzzy inference system‐based rotor position control of brushless DC motor." IET Power Electronics 9, no. 11: 2229-2239.

Journal article
Published: 01 June 2016 in Engineering Science and Technology, an International Journal
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In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation

ACS Style

K. Premkumar; B.V. Manikandan. Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Engineering Science and Technology, an International Journal 2016, 19, 818 -840.

AMA Style

K. Premkumar, B.V. Manikandan. Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Engineering Science and Technology, an International Journal. 2016; 19 (2):818-840.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2016. "Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor." Engineering Science and Technology, an International Journal 19, no. 2: 818-840.

Journal article
Published: 10 August 2015 in Journal of Intelligent & Fuzzy Systems
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ACS Style

K. Premkumar; B.V. Manikandan. GA-PSO optimized online ANFIS based speed controller for Brushless DC motor. Journal of Intelligent & Fuzzy Systems 2015, 28, 2839 -2850.

AMA Style

K. Premkumar, B.V. Manikandan. GA-PSO optimized online ANFIS based speed controller for Brushless DC motor. Journal of Intelligent & Fuzzy Systems. 2015; 28 (6):2839-2850.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2015. "GA-PSO optimized online ANFIS based speed controller for Brushless DC motor." Journal of Intelligent & Fuzzy Systems 28, no. 6: 2839-2850.

Journal article
Published: 01 July 2015 in Applied Soft Computing
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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.

ACS Style

K. Premkumar; B.V. Manikandan. Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System. Applied Soft Computing 2015, 32, 403 -419.

AMA Style

K. Premkumar, B.V. Manikandan. Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System. Applied Soft Computing. 2015; 32 ():403-419.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2015. "Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System." Applied Soft Computing 32, no. : 403-419.

Journal article
Published: 01 June 2015 in Neurocomputing
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ACS Style

K. Premkumar; B.V. Manikandan. Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing 2015, 157, 76 -90.

AMA Style

K. Premkumar, B.V. Manikandan. Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing. 2015; 157 ():76-90.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2015. "Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor." Neurocomputing 157, no. : 76-90.

Book chapter
Published: 20 November 2014 in Lecture Notes in Electrical Engineering
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In this paper, Online Fuzzy Logic Supervised Learning of Radial Basis Function Neural Network (RBFNN) based speed controller for Brushless DC (BLDC) motor is presented. The Fuzzy PID controller is acting as supervisor for RBFNN controller. Dynamic speed response is analyzed for BLDC motor with conventional PID controller and proposed controller. Rise time, peak overshoot, recovery time and steady state error are measured and analyzed for above controller. From the results, the proposed controller outperforms than PID controller.

ACS Style

K. Premkumar; B.V. Manikandan. Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor. Lecture Notes in Electrical Engineering 2014, 1397 -1405.

AMA Style

K. Premkumar, B.V. Manikandan. Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor. Lecture Notes in Electrical Engineering. 2014; ():1397-1405.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2014. "Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor." Lecture Notes in Electrical Engineering , no. : 1397-1405.

Journal article
Published: 01 August 2014 in Neurocomputing
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ACS Style

K. Premkumar; B.V. Manikandan. Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor. Neurocomputing 2014, 138, 260 -270.

AMA Style

K. Premkumar, B.V. Manikandan. Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor. Neurocomputing. 2014; 138 ():260-270.

Chicago/Turabian Style

K. Premkumar; B.V. Manikandan. 2014. "Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor." Neurocomputing 138, no. : 260-270.

Conference paper
Published: 01 February 2013 in 2013 International Conference on Power, Energy and Control (ICPEC)
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A novel method for speed control of brushless dc motor using adaptive fuzzy logic and PI control algorithms has been presented in this paper. Fuzzy logic and PI controllers are formulated and designed using MATLAB toolbox. The parameters such as rise time, peak overshoot, recovery time, settling time and steady state error of a brushless DC motor are taken for analyzing the performance of the proposed controller. The simulation result demonstrated that the response of brushless dc motor with adaptive fuzzy logic shows satisfactory and well damped performance compared to classical PI controller.

ACS Style

K. Premkumar; B. V. Manikandan. Adaptive fuzzy logic speed controller for brushless DC motor. 2013 International Conference on Power, Energy and Control (ICPEC) 2013, 290 -295.

AMA Style

K. Premkumar, B. V. Manikandan. Adaptive fuzzy logic speed controller for brushless DC motor. 2013 International Conference on Power, Energy and Control (ICPEC). 2013; ():290-295.

Chicago/Turabian Style

K. Premkumar; B. V. Manikandan. 2013. "Adaptive fuzzy logic speed controller for brushless DC motor." 2013 International Conference on Power, Energy and Control (ICPEC) , no. : 290-295.