The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
M.t. Hayajneh - One of the best experts on this subject based on the ideXlab platform.
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A fuzzy gain scheduling scheme for the PIIσD controllers
International Journal of Modelling and Simulation, 2004Co-Authors: S.m. Radaideh, M.t. HayajnehAbstract:AbstractThis article proposes a new fuzzy gain scheduling scheme for the PII>rD controller. Fuzzy IF-THEN rules are used online to adjust the parameters of the PID controller based on the system error and its derivative. Simulation results clearly reveal that the proposed scheme outperforms the previously developed controllers. Performance metrics used in the evaluation are: settling time, 1% Peak Overshoot, and integral of the time multiplied by the square error. Computer simulations numerical examples are demonstrated using first-order plant with signal transmission delay and third-order plant.
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A New Fuzzy Gain Scheduling Scheme for the PID Controllers
Intelligent Automation and Soft Computing, 2003Co-Authors: S.m. Radaideh, M.t. HayajnehAbstract:Abstract In this paper, a new fuzzy gain scheduling scheme for the PID controller have been proposed. Fuzzy IF-THEN rules aze used on-line to adjust the parameters of the PID controller based on the system error and its derivative. In terms of settling time, one percent Peak Overshoot and the integral of the time multiplied by the absolute error, the simulation results clearly indicate that the performance of the proposed scheme is better than the Ziegler-Nichols PID controller [1] and the fuzzy gain scheduling scheme of PID controller [2]. For illustration and compazison, numerical examples, using third and fourth order plants, are presented.
Shilpa Y. Sondkar - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Performance of PID Controller and State Feedback Controller for Flow Control Loop
2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018Co-Authors: Kasturi S. Pawar, Meghraj V. Palwe, Sonali B. Ellath, Shilpa Y. SondkarAbstract:Classical PID control is most commonly used in a closed control feedback loop to regulate the process. This paper proposes the state feedback controller for the flow control loop. A comparative study of performance of PID controller and the state feedback gain controller is done by implementing both the controller on the flow loop. The real time implementation is done by interfacing the flow loop to LabVIEW via the NI-DAQ card. Both PID and state feedback controller are implemented in MATLAB in real time. It is observed that the state feedback controller has less rise time and no Peak Overshoot as compared to PID controller.
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Comparative study of real time implementation of LabVIEW based MPC controller and PID controller for flow control loop
2017 2nd International Conference for Convergence in Technology (I2CT), 2017Co-Authors: Chetan D. Jichkar, Shilpa Y. SondkarAbstract:This paper presents the comparative study of Model Predictive Controller (MPC) with PID Controller implemented for flow control loop in LABVIEW. Classical PID controller is mostly used in process instrumentation yet tuning of PID controller is the crucial part to be dealt with. Moreover, changes in the parameters of the loop components affects the tuning of the controller, hence over a period of time retuning of PID controller is required. On the other hand MPC controller works on the process model which predicts and optimizes the process performance. Here, Flow control loop performance was studied for MPC and PID controller. MPC worked on 1st order state space system model and PID was tuned using Internal Model Control (IMC) method. The result of implementation shows that, MPC controller performs much better than PID controller. Set point of with less settling time and zero Peak Overshoot was observed in MPC whereas PID has comparatively more settling time and Peak Overshoot.
B.v. Manikandan - One of the best experts on this subject based on the ideXlab platform.
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Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor
Lecture Notes in Electrical Engineering, 2014Co-Authors: K. Premkumar, B.v. ManikandanAbstract: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.
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Adaptive fuzzy logic speed controller for brushless DC motor
2013 International Conference on Power Energy and Control (ICPEC), 2013Co-Authors: K. Premkumar, B.v. ManikandanAbstract: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.
S.m. Radaideh - One of the best experts on this subject based on the ideXlab platform.
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A fuzzy gain scheduling scheme for the PIIσD controllers
International Journal of Modelling and Simulation, 2004Co-Authors: S.m. Radaideh, M.t. HayajnehAbstract:AbstractThis article proposes a new fuzzy gain scheduling scheme for the PII>rD controller. Fuzzy IF-THEN rules are used online to adjust the parameters of the PID controller based on the system error and its derivative. Simulation results clearly reveal that the proposed scheme outperforms the previously developed controllers. Performance metrics used in the evaluation are: settling time, 1% Peak Overshoot, and integral of the time multiplied by the square error. Computer simulations numerical examples are demonstrated using first-order plant with signal transmission delay and third-order plant.
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A New Fuzzy Gain Scheduling Scheme for the PID Controllers
Intelligent Automation and Soft Computing, 2003Co-Authors: S.m. Radaideh, M.t. HayajnehAbstract:Abstract In this paper, a new fuzzy gain scheduling scheme for the PID controller have been proposed. Fuzzy IF-THEN rules aze used on-line to adjust the parameters of the PID controller based on the system error and its derivative. In terms of settling time, one percent Peak Overshoot and the integral of the time multiplied by the absolute error, the simulation results clearly indicate that the performance of the proposed scheme is better than the Ziegler-Nichols PID controller [1] and the fuzzy gain scheduling scheme of PID controller [2]. For illustration and compazison, numerical examples, using third and fourth order plants, are presented.
T.k. Sindhu - One of the best experts on this subject based on the ideXlab platform.
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Automatic Generation Control of Single Area Thermal Power System with Fractional Order PID (PIλDμ) Controllers
IFAC Proceedings Volumes, 2014Co-Authors: C. Ismayil, Kumar R. Sreerama, T.k. SindhuAbstract:Abstract This paper proposes a fractional order PID (FOPID) controller for the supplementary automatic generation control (AGC) of a single area thermal power system having non-reheat turbines. The parameters of fractional PID controller are determined using genetic algorithm (GA). The effectiveness of the proposed controller is established by comparing its performance with the conventional integer order integral, PI and PID controllers based AGC. The investigations reveal that the proposed fractional order controller is better than the integer order controllers in terms of settling time, Peak Overshoot, steady state error and mean of sum of squared errors (MSSE).