Multi-objective flower pollination algorithm for optimal design of fractional order controller for robust isolated steam turbine

Kumar, Parvesh; Katal, Nitish; Narayan, Shiv

doi:10.1109/icpeices.2016.7853113

Cited by 3 publications

(8 citation statements)

References 14 publications

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“…However, FPA was able to produce the optimal global peaks under the strong shading condition which PSO could not achieve, thereby justifying FPA as a more suitable algorithm [83]. Murdianto et al [116] investigated the prospect of adding a DC-DC BUCK converter to a DC microgrid to improve the grid stability. Murdianto et al [116] tested the hypothesis by using the FPA to optimize the MPPT tracking of a proportional integral (PI) controller in the system.…”

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

“…Murdianto et al [116] investigated the prospect of adding a DC-DC BUCK converter to a DC microgrid to improve the grid stability. Murdianto et al [116] tested the hypothesis by using the FPA to optimize the MPPT tracking of a proportional integral (PI) controller in the system. The results displayed that the FPA optimized PI controller was capable of accurately tracking the MPPT and thus resulted in a more stable system (due the regulated voltage).…”

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

“…The results displayed that the FPA optimized PI controller was capable of accurately tracking the MPPT and thus resulted in a more stable system (due the regulated voltage). Murdianto et al [116] also highlighted the fact that using FPA to track the MPPT is advantageous due to the simplicity to implement as compared to conventional techniques such as IC and P&O algorithms due to it requiring far less parameters to tune [116]. Murdianto et al [84] investigates FPA MPPT capabilities further by attempting to track the MPP of a SEPIC converter under partial shading conditions.…”

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

“…Yousri et al [117] conducted the simulation-based test on a solar generation unit under various shading conditions. The results obtained in [117] showed that C-FPA variants were able track the GMPP efficiently and assisted in producing a [66], [115], [83], [116], [84], [117], [118] Conventional/Renewable energy generation optimization -use of FPA to optimize renewable/ conventional energy generation units.…”

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

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Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

et al. 2022

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Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

Section: Renewable Energy Generation Efficiencymentioning

confidence: 99%

See 2 more Smart Citations

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

et al. 2022

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“…A novel LQG/LQR controller design based on multi‐objective formulation has been proposed, in which the problem was formulated and solved by making trade‐off between multiple objectives in time as well as frequency domain 24 . In most of the research with multi‐objective formulation, the results have been obtained with a meta‐heuristic optimization algorithm to get the optimal solution 25 . The important meta‐heuristics are classified as: physics‐based, evolutionary, and swarm intelligence (SI) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fractional‐order filter‐based enhanced full state feedback control design with multi‐objective formulation using grey wolf optimization algorithm

Kumar

Dhillon

Kumar³

2022

Optim Control Appl Methods

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A perturbed fractional-order filter (FOF)-based LQR control design with multiple performance indices is proposed in this paper. Three variants of the FOF-based linear quadratic regulator (LQR) system have been proposed along with choices on the commensurate order of state feedback law. The multi-objective problem formulation has been done with three performance objectives to address the different issues of design. The first performance index constitutes a weighted sum of ITAE (Integral Time of Absolute Error) and the difference between the eigenvalues of the plant and the LQR controlled plant, chosen to minimize the large oscillations as well as relative stability with respect to eigenvalues. A new lower bound of the singular values have been addressed to ensure the robust stability of the system through the formulation of a second performance objective. It is the minimization of singular values of the return ratio matrix at the plant's input. The third and the last performance index aims minimization of maximum singular values of the perturbation transfer function at the plant's output, so as to guarantee no closed-loop right half plane zeros. The multi-objective optimization problem is solved and the optimal solutions are obtained via Grey Wolf Optimization Algorithm. The simulation results validate the performance and effectiveness of the proposed control design.

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Multi-Objective Optimization Based Multi-Objective Controller Tuning Method with Robust Stabilization of Fractional Calculus CSTR

Sudha

2021

Wseas Transactions on Systems and Control

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In Continuous Stirred Tank Reactor (CSTR) have Fractional order PID with the nominal order PID controller has been used to Multi-Criteria Decision Making (MCDM) and EMO (Evolutionary Multi-objective Optimization) by adjustment of control parameters like Hybrid methods in Multi objective optimization. But, this Fractional order PID with the nominal PID controller has maximum performance estimation. Proposed research work focused the Flower Pollination Algorithm based on Multi objective optimization with Genetic evaluation and Fractional order PID with the nominal PID controller is provides CSTR results. When a flower is displayed to maximum variations in this practical state, the Genetic evaluation has been used to identify the variations. The FPID (Flower Pollination Integral Derivative) is used for tuning the parameters of a Fractional order PID with the nominal PID controller for each region to improve the multi-criteria decision making. FPID also denoted as Flower Optimization Integral Derivative (FOID). The Genetic evaluation scheduler has been combined with multiple local linear Fractional order PID with the nominal PID controller to check the stability of loop for entire regions with various levels of temperatures. MATLAB results demonstrate that the feasibility of using the proposed Fractional order PID with the nominal PID controller compared than the existing PID controller, and it shows the FOID attained better results.

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Multi-objective flower pollination algorithm for optimal design of fractional order controller for robust isolated steam turbine

Cited by 3 publications

References 14 publications

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

Fractional‐order filter‐based enhanced full state feedback control design with multi‐objective formulation using grey wolf optimization algorithm

Multi-Objective Optimization Based Multi-Objective Controller Tuning Method with Robust Stabilization of Fractional Calculus CSTR

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