The Experts below are selected from a list of 59349 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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bat algorithm optimized fuzzy pd based Speed Controller for brushless direct current motor
Engineering Science and Technology an International Journal, 2016Co-Authors: K Premkumar, B V ManikandanAbstract:Abstract 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.
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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.
Seung-ki Sul - One of the best experts on this subject based on the ideXlab platform.
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dsp based self tuning ip Speed Controller with load torque compensation for rolling mill dc drive
IEEE Transactions on Industrial Electronics, 1995Co-Authors: Seung-ki SulAbstract:This paper describes the design and the implementation of a self-tuning integral-proportional (IP) Speed Controller for a rolling mill DC motor drive system, based on a 32-bit floating point digital signal processor (DSP)-TMS 320C30. To get a better transient response than conventional proportional-integral (PI) and/or integral-proportional (IP) Speed control in the presence of transient disturbance and/or parameter variations, an adaptive self-tuning IP Speed control with load torque feedforward compensation was used. The model parameters, related to motor and load inertia and damping coefficient, were estimated online by using recursive extended least squares (RELS) estimation algorithm. On the basis of the estimated model parameters and a pole-placement design, a control signal was calculated. Digital simulation and experimental results showed that the proposed Controller possesses excellent adaptation capability under parameter change and a better transient recovery characteristic than a conventional PI/IP Controller under load change. >
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Kalman Filter and LQ Based Speed Controller for Torsional Vibration Suppression in a 2-Mass Motor Drive System
IEEE Transactions on Industrial Electronics, 1995Co-Authors: Jun Keun Ji, Seung-ki SulAbstract:In this paper, a high-performance Speed control for torsional vibration suppression in a 2-mass motor drive system, like a rolling mill which has a long shaft and large loadside mass or a robot arm which has flexible coupling, was studied. The Speed control method which has better control response than a typical one in command following, torsional vibration suppression, disturbance rejection, and robustness to parameter variation, was proposed. The performance of command following, torsional vibration suppression, and robustness to parameter variation was satisfied by using a Kalman filter and LQ based Speed control with an integrator. Also, disturbance rejection performance was improved through load torque compensation. Through various experiments of a real 22 kW field oriented controlled AC motor drive system having 2-mass mechanical system, the characteristics of the proposed Speed Controller and typical PI Speed Controller were compared and analyzed
K Szabat - One of the best experts on this subject based on the ideXlab platform.
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adaptive sliding mode neuro fuzzy control of the two mass induction motor drive without mechanical sensors
IEEE Transactions on Industrial Electronics, 2010Co-Authors: Teresa Orlowskakowalska, M Dybkowski, K SzabatAbstract:In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive Speed Controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy Controller is used as the Speed Controller, whose connective weights are trained online according to the error between the estimated motor Speed and the Speed given by the reference model. The Speed of the vector-controlled IM is estimated using the MRASCC rotor Speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor Speed given by the proposed Speed estimator. Simulation results are verified by experimental tests over a wide range of motor Speed and drive parameter changes.
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optimization of fuzzy logic Speed Controller for dc drive system with elastic joints
IEEE Transactions on Industry Applications, 2004Co-Authors: Teresa Orlowskakowalska, K SzabatAbstract:This paper deals with the analysis of a DC drive system with elastic joints and different Speed Controllers. The control structure with one and two Speed feedbacks was analyzed. The dynamics of the drive system with classical proportional-integral (PI) and fuzzy-logic (FL) Speed Controllers was compared. Parameters of the classical PI and FL Speed Controllers were optimized using the same control indexes. Controllers were parameterised using the hybrid genetic-gradient algorithm. The simulation results for different parameters and operation modes of the drive system were demonstrated and compared.
Teresa Orlowskakowalska - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of the sensorless adaptive sliding mode neuro fuzzy control of the induction motor drive with mras type Speed estimator
Bulletin of The Polish Academy of Sciences-technical Sciences, 2012Co-Authors: Teresa Orlowskakowalska, M DybkowskiAbstract:This paper discusses a model reference adaptive sliding-mode control of the sensorless vector controlled induction motor drive in a wide Speed range. The adaptive Speed Controller uses on-line trained fuzzy neural network, which enables very fast tracking of the changing Speed reference signal. This adaptive sliding-mode neuro-fuzzy Controller (ASNFC) is used as a Speed Controller in the direct rotor-field oriented control (DRFOC) of the induction motor (IM) drive structure. Connective weights of the Controller are trained on-line according to the error between the actual Speed of the drive and the reference model output signal. The rotor flux and Speed of the vector controlled induction motor are estimated using the model reference adaptive system (MRAS) – type estimator. Presented simulation results are verified by experimental tests performed on the laboratory-rig with DSP Controller.
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adaptive sliding mode neuro fuzzy control of the two mass induction motor drive without mechanical sensors
IEEE Transactions on Industrial Electronics, 2010Co-Authors: Teresa Orlowskakowalska, M Dybkowski, K SzabatAbstract:In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive Speed Controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy Controller is used as the Speed Controller, whose connective weights are trained online according to the error between the estimated motor Speed and the Speed given by the reference model. The Speed of the vector-controlled IM is estimated using the MRASCC rotor Speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor Speed given by the proposed Speed estimator. Simulation results are verified by experimental tests over a wide range of motor Speed and drive parameter changes.
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optimization of fuzzy logic Speed Controller for dc drive system with elastic joints
IEEE Transactions on Industry Applications, 2004Co-Authors: Teresa Orlowskakowalska, K SzabatAbstract:This paper deals with the analysis of a DC drive system with elastic joints and different Speed Controllers. The control structure with one and two Speed feedbacks was analyzed. The dynamics of the drive system with classical proportional-integral (PI) and fuzzy-logic (FL) Speed Controllers was compared. Parameters of the classical PI and FL Speed Controllers were optimized using the same control indexes. Controllers were parameterised using the hybrid genetic-gradient algorithm. The simulation results for different parameters and operation modes of the drive system were demonstrated and compared.
M A Hannan - One of the best experts on this subject based on the ideXlab platform.
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a quantum lightning search algorithm based fuzzy Speed Controller for induction motor drive
IEEE Access, 2018Co-Authors: M A Hannan, Jamal Abd Ali, Aini Hussain, Fazida Hanim Hasim, Ungku Anisa Ungku Amirulddin, M N Uddin, Frede BlaabjergAbstract:This paper presents a quantum lightning search algorithm (QLSA) -based optimization technique for controlling Speed of the induction motor (IM) drive. The developed QLSA is implemented in fuzzy logic Controller to generate suitable input and output fuzzy membership function for IM drive Speed Controller. The main objective of this paper is to develop QLSA-based fuzzy (QLSAF) Speed Controller to minimise the mean absolute error in order to improve the performance of the IM drive with changes in Speed and mechanical load. The QLSAF-based Speed Controller is implemented in simulation model in the MATLAB/Simulink environment and the prototype is fabricated and experimentally tested in a fully integrated DSP for controlling the IM drive system. The experimental results of the developed QLSAF Speed Controller are compared with the simulation results under different performance conditions. Several experimental results show that there are good agreement of the Controller parameters, SVPWM signals, and different types of Speed responses and stator currents with the simulation results, which are verified and validated the performance of the proposed QLSAF Speed Controller. Also, the proposed QLSAF Speed Controller outperforms other studies with settling time in simulation and in experimental implementation, which validates the Controller performance as well.
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optimized Speed Controller for induction motor drive using quantum lightning search algorithm
IEEE International Conference on Power and Energy, 2016Co-Authors: Jamal Abd Ali, M A Hannan, Azah MohamedAbstract:This paper presents an improve proportional-integral-derivative (PID) Controller design technique for controlling a three-phase induction motor (TIM) Speed drive using quantum lightning search algorithm (QLSA). This proposed Controller avoids the exhaustive conventional trial- and-error procedure for obtaining PID parameters. Objective function using in the proposed Controller is mean absolute error (MAE) to enhance the TIM Speed performance under sudden change of the Speed and load conditions. The QLSA is used to improve two Controller system PID and PI Controllers in the TIM drive. Moreover, the QLSA algorithm comperes with three optimization algorithms, namely, lightning search algorithm (LSA), the backtracking search algorithm (BSA), the particle swarm optimization (PSO). Designed and validated the simulation model by using a MATLAB/Simulink environment. Results show that the QLSA-based PID and PI Speed Controller is achieved better results than the other optimization Controllers through reduce of damping capability, enhance the transient response, minimize the MAE, root mean square error (RMSE) and standard division (SD) of the Speed response.
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fuzzy logic Speed Controller optimization approach for induction motor drive using backtracking search algorithm
Measurement, 2016Co-Authors: M A Hannan, Azah Mohamed, Maher G M AbdolrasolAbstract:Abstract This paper presents an adaptive fuzzy logic Controller (FLC) design technique for controlling an induction motor Speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in Speed Controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor Speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in Speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed Controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF Controller is better than the GSA and PSO Controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and Speeds.
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a novel quantum behaved lightning search algorithm approach to improve the fuzzy logic Speed Controller for an induction motor drive
Energies, 2015Co-Authors: Jamal Abd Ali, M A Hannan, Azah MohamedAbstract:This paper presents a novel lightning search algorithm (LSA) using quantum mechanics theories to generate a quantum-inspired LSA (QLSA). The QLSA improves the searching of each step leader to obtain the best position for a projectile. To evaluate the reliability and efficiency of the proposed algorithm, the QLSA is tested using eighteen benchmark functions with various characteristics. The QLSA is applied to improve the design of the fuzzy logic Controller (FLC) for controlling the Speed response of the induction motor drive. The proposed algorithm avoids the exhaustive conventional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive input and output MFs are implemented in the fuzzy Speed Controller design to formulate the objective functions. Mean absolute error (MAE) of the rotor Speed is the objective function of optimization Controller. An optimal QLSA-based FLC (QLSAF) optimization Controller is employed to tune and minimize the MAE, thereby improving the performance of the induction motor with the change in Speed and mechanical load. To validate the performance of the developed Controller, the results obtained with the QLSAF are compared to the results obtained with LSA, the backtracking search algorithm (BSA), the gravitational search algorithm (GSA), the particle swarm optimization (PSO) and the proportional integral derivative Controllers (PID), respectively. Results show that the QLASF outperforms the other control methods in all of the tested cases in terms of damping capability and transient response under different mechanical loads and Speeds.
