scholarly journals Fractional order PID controller adaptation for PMSM drive using hybrid grey wolf optimization

2021 ◽  
Vol 12 (2) ◽  
pp. 745
Author(s):  
Yasser Ahmed ◽  
Ayman Hoballah ◽  
Essam Hendawi ◽  
Sattam Al Otaibi ◽  
Salah K. Elsayed ◽  
...  
Keyword(s):  
Fractional Order ◽  
Permanent Magnet Synchronous Motor ◽  
Fast Response ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Transient Time ◽  
Speed Controller ◽  
Optimal Controllers ◽  
Steady State Time ◽  
Indirect Field Oriented Control

In this paper, the closed loop speed controller parameters are optimized for the permanent magnet synchronous motor (PMSM) drive on the basis of the indirect field-oriented control (IFOC) technique. In this derive system under study, the speed and current controllers are implemented using the fractional order proportional, integral, and derivative (FOPID) controlling technique. FOPID is considered as efficient techniques for ripple minimization. The hybrid grey wolf optimizer (HGWO) is applied to obtain the optimal controllers in case of implementing conventional PID as well as FOPID controllers in the derive system. The optimal controller parameters tend to enhance the drive response as ripple content in speed and current, either during steady state time or transient time. The drive system is modeled and tested under various operating condition of load torque and speed. Finally, the performance for PID and FOPID are evaluated and compared within MATLAB/Simulink environment. The results attain the efficacy of the operating performance with the FOPID controller. The result shows a fast response and reduction of ripples in the torque and the current.

scholarly journals Design of Fractional Order PID (FOPID)for Load Frequency Control Via IMC & Grey Wolf Algorithm (GWO), for a Non-Reheated Power System by Comparing with the Big Bang Big Crunch (BBBC) Optimization

2020 ◽  
Vol 9 (1) ◽  
pp. 1253-1260
Keyword(s):  
Fractional Order ◽  
Frequency Control ◽  
Big Bang ◽  
Load Frequency Control ◽  
Frequency Variation ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Electrical System ◽  
Load Frequency ◽  
Fractional Order Pid

In this paper work deals about the application of Grey Wolf Optimizer (GWO) for optimization of fractional order PID (FOPID) controlling device to the frequency disturbance, of system load in the one (or) single area non re-heated electrical system and also comparison to the non re-heated BBBC optimization outputs. In this BBBC optimization we have the two bounding cases (low & upper), they are before and after the perturbation cases. And also we observed that the BBBC output responses. After finding the BBBC outputs we observed that the settling time value of load frequency of BBBC is more when compared with the GWO. This problem is resolved by designing of FOPID via GWO algorithm. The Grey Wolf Optimization is well known meta-heuristic algorithm and has been previously used for optimization of various conventional PID and FOPID controllers. In this paper the GWO is used for optimization of FOPID controller to the load frequency variation in the electrical system for non reheated turbine electrical system .the execution outputs of the proposed controlled method also validated to the other existing techniques

scholarly journals Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 177
Author(s):  
Mateusz Zychlewicz ◽  
Radoslaw Stanislawski ◽  
Marcin Kaminski
Keyword(s):  
Neural Network ◽  
Grey Wolf Optimizer ◽  
State Variables ◽  
Grey Wolf ◽  
Neural Controller ◽  
Mass System ◽  
Numerical Tests ◽  
Speed Controller ◽  
The Neural Network ◽  
Oscillation Damping

In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally.

Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm

2016 ◽  
Vol 6 (3) ◽  
pp. 1353 ◽  
Author(s):  
Yannis L Karnavas ◽  
Ioannis D Chasiotis ◽  
Emmanouil L Peponakis
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnets ◽  
Permanent Magnet Synchronous Motor ◽  
Optimization Method ◽  
Synchronous Motor ◽  
Optimization Techniques ◽  
Attractive Alternative ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Low Speed

Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “<em>Grey Wolf Optimizer</em>” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques.

Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer

Robotica ◽  
2019 ◽  
Vol 38 (4) ◽  
pp. 605-616 ◽  
Author(s):  
Hossein Komijani ◽  
Mojtaba Masoumnezhad ◽  
Morteza Mohammadi Zanjireh ◽  
Mahdi Mir
Keyword(s):  
Fractional Order ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Grey Wolf Optimizer ◽  
Sliding Mode Controller ◽  
Grey Wolf ◽  
Robust Controller ◽  
The Stability ◽  
Proportional Derivative Controller

SUMMARYThis paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

scholarly journals A Fractional Order Control Strategy for LFC via Big Bang Big Crunch & Grey Wolf Optimization Algorithms

2020 ◽  
Vol 184 ◽  
pp. 01016
Author(s):  
Dola Gobinda Padhan ◽  
Syed Sarfaraz Nawaz ◽  
Padmanabhuni Ravikanth
Keyword(s):  
Fractional Order ◽  
Big Bang ◽  
Internal Model Control ◽  
Settling Time ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Control ◽  
Big Crunch ◽  
Simulation Results

The Grey Wolf Optimization (GWO) is well known meta-heuristic algorithm and has been previously used for optimization of various conventional PID and FOPID controllers. This paper deals with the application of Grey Wolf Optimizer (GWO) algorithm and internal model control (IMC) for optimization of fractional order PID (FOPID) controller parameters to the load disturbance of system. This is applicable for one (or) single area non reheated electrical system. The simulation results are compared with the non re-heated Big Bang Big Crunch (BBBC) optimization outputs. In this paper, BBBC optimization has the two bounding cases (lower & upper), they are before and after the perturbation cases. Also it is observed that in case of the BBBC output responses, the settling time value of load frequency is more when compared with the GWO. From the simulation results it is concluded that GWO out performs as compared with BBBC as it produces less error and settling time.

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