Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer

Verma, Santosh Kumar; Yadav, Shekhar; Nagar, Shyam Krishna

doi:10.1007/s40313-017-0305-3

Cited by 73 publications

(37 citation statements)

References 36 publications

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“…The rules mention that EI value becomes smaller than 0.01, or reaches 15 sequential iterations. Then, we compare the results obtained by a proposed algorithm with three common FOPID optimization methods in the literature ( [47], [62], [92], [93]) including the PSO metaheuristic [59] (i.e. when directly used in optimization procedure), Grey Wolf Optimizer (GWO) [94] and Ant Lion Optimizer (ALO) [95].…”

Section: Resultsmentioning

confidence: 99%

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

Parnianifard¹,

Wuttisittikulkij²,

Zemouche³

et al. 2020

Preprint

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This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to changeability of a target of control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.

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Section: Resultsmentioning

confidence: 99%

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

Parnianifard¹,

Wuttisittikulkij²,

Zemouche³

et al. 2020

Preprint

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“…They are the last wolves allowed to eat. It seems like omega wolves do not have an important role in the group but it is observed that the group has some problems like internal fighting in the absence of omegas [17], [18].…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Linear Quadratic Regulator Design for Position Control of an Inverted Pendulum by Grey Wolf Optimizer

Erkol¹

2018

ijacsa

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Abstract-In this study, a linear quadratic regulator (LQR) based position controller is designed and optimized for an inverted pendulum system. Two parameters, vertical pendulum angle and horizontal cart position, must be controlled together to move a pendulum to desired position. PID controllers are conventionally used for this purpose and two different PID controllers must be used to move the pendulum. LQR is an alternative method. Angle and position of inverted pendulum can be controlled using only one LQR. Determination of Q and R matrices is the main problem when designing an LQR and they must be minimized a defined performance index. Determination of the Q and R matrices is generally made by trial and error method but finding the optimum parameters using this method is difficult and not guaranty. An optimization algorithm can be used for this purpose and in this way; it is possible to obtain optimum controller parameters and high performance. That's why an optimization method, grey wolf optimizer, is used to tune controller parameters in this study.

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“…On the other hand, a Grey Wolf Optimizer (GWO) [10], which is in the class of nature inspired based optimization, can be also considered as a potential tool for improving energy production of wind plant. This is because the GWO has been successfully solved various types of real world problems, such as optimal reactive power coordination [11], vehicle engine connecting rod [12], unmanned aerial vehicles [13,14], facial image super-resolution [15,16], surface waves estimation [17], PID controller tuning [18,19]. The essential feature of the GWO algorithm is that it inspires the hierarchy, leadership and hunting techniques of grey wolves.…”

Section: Introductionmentioning

confidence: 99%

A modified grey wolf optimizer for improving wind plant energy production

Tumari

Suid

Ahmad

2020

IJEECS

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<span>The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods.</span>

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Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer

Cited by 73 publications

References 36 publications

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

Linear Quadratic Regulator Design for Position Control of an Inverted Pendulum by Grey Wolf Optimizer

A modified grey wolf optimizer for improving wind plant energy production

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