Multi-Objective Fractional-Order Control Design for Solar Collector Systems Under Parameter Perturbations and Stability Constraints
- 1Department of Mechanical Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Abstract
The primary objective of a parabolic distributed solar collector system (P-DSCS) is to regulate fluid heat generation so that it tracks a reference trajectory despite non-uniform solar irradiance. Fractional-order proportional-integral-derivative (FOPID) controllers offer superior dynamic flexibility to handle these complex thermal transients. However, existing tuning strategies for P-DSCS collapse frequency-domain requirements into an optimization problem solved by standard algorithms, masking critical trade-offs between competing performances and omitting closed-loop stability verification. To overcome these limitations, this paper presents an original multi-objective optimization framework for FOPID controlled P-DSCS. The tuning problem is formulated as a three-objective Optimization Problem that simultaneously minimizes phase-margin slope sensitivity for iso-damping robustness, peak sensitivity for low-frequency disturbance rejection, and complementary sensitivity for high-frequency noise attenuation. The Multi-Objective Imperialist Competitive Algorithm (MOICA) extracts a three-dimensional Pareto front, offering optimal control trade-offs. Comparative simulations against PID and literature algorithms (Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II)) validate the superior performance of the MOICA-FOPID framework. MOICA consistently outperforms all algorithms across all metrics, improving iso-damping robustness by 38.99%. It also reduces high-frequency noise by 25.66%, while enhancing low-frequency disturbance rejection by 10.87%. Moreover, the optimal MOICA-FOPID controller maintained superior robustness despite P-DSCS gain and time-constant fluctuations.
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BibTeX
@article{alghafis2026multiobjective,
title = {{Multi-Objective Fractional-Order Control Design for Solar Collector Systems Under Parameter Perturbations and Stability Constraints}},
author = {Abdullah Alghafis and Mohamed Nejlaoui},
journal = {Fractal and Fractional},
year = {2026},
month = oct,
doi = {10.3390/fractalfract10100707},
url = {https://www.mdpi.com/2504-3110/10/10/707},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-Objective Fractional-Order Control Design for Solar Collector Systems Under Parameter Perturbations and Stability Constraints
AU - Abdullah Alghafis
AU - Mohamed Nejlaoui
PY - 2026
DA - 2026/10/08
JO - Fractal and Fractional
DO - 10.3390/fractalfract10100707
UR - https://www.mdpi.com/2504-3110/10/10/707
AB - The primary objective of a parabolic distributed solar collector system (P-DSCS) is to regulate fluid heat generation so that it tracks a reference trajectory despite non-uniform solar irradiance. Fractional-order proportional-integral-derivative (FOPID) controllers offer superior dynamic flexibility to handle these complex thermal transients. However, existing tuning strategies for P-DSCS collapse frequency-domain requirements into an optimization problem solved by standard algorithms, masking critical trade-offs between competing performances and omitting closed-loop stability verification. To overcome these limitations, this paper presents an original multi-objective optimization framework for FOPID controlled P-DSCS. The tuning problem is formulated as a three-objective Optimization Problem that simultaneously minimizes phase-margin slope sensitivity for iso-damping robustness, peak sensitivity for low-frequency disturbance rejection, and complementary sensitivity for high-frequency noise attenuation. The Multi-Objective Imperialist Competitive Algorithm (MOICA) extracts a three-dimensional Pareto front, offering optimal control trade-offs. Comparative simulations against PID and literature algorithms (Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II)) validate the superior performance of the MOICA-FOPID framework. MOICA consistently outperforms all algorithms across all metrics, improving iso-damping robustness by 38.99%. It also reduces high-frequency noise by 25.66%, while enhancing low-frequency disturbance rejection by 10.87%. Moreover, the optimal MOICA-FOPID controller maintained superior robustness despite P-DSCS gain and time-constant fluctuations.
SN - 2504-3110
ER -