Improved Load-Frequency Control Using a Fuzzy Model Predictive Control Method Enhanced by Iterative Learning in Hybrid Power Systems

Improved Load-Frequency Control Using a Fuzzy Model Predictive Control Method Enhanced by Iterative Learning in Hybrid Power Systems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2026 V1 Latest version Share on Improved Load-Frequency Control Using a Fuzzy Model Predictive Control Method Enhanced by Iterative Learning in Hybrid Power Systems Author : Adil Albarghooth 0009-0001-5527-7694 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177017774.46965826/v1 149 views 68 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Abstract: This paper proposes a hierarchical hybrid control framework, termed Fuzzy Model Predictive Control with Iterative Learning (FMPC–IL), to enhance load–frequency control (LFC) in single-area hybrid power systems with high renewable penetration. The architecture integrates three layers: (i) a supervisory fuzzy logic controller (FLC) that adaptively tunes the MPC cost-function weights (Qⓜ,R)using the frequency error and its change, improving robustness under operating uncertainties; (ii) a constrained model predictive control (MPC) layer that computes the optimal control action while explicitly enforcing actuator and operational limits; and (iii) an iterative learning control (ILC) layer that exploits repetitive daily patterns in load and renewable profiles to generate a feedforward compensation signal updated cycle-to-cycle. The nonlinear governor–turbine limitations, including valve deadband, actuator saturation, and generation rate constraints (GRC), are represented in the simulation plant, whereas a linearized discrete-time model is used as the internal prediction model for MPC optimization. The proposed FMPC–IL strategy is evaluated in MATLAB/Simulink under stochastic load disturbances, renewable fluctuations, and parametric uncertainties. The results demonstrate improved frequency regulation and robustness compared with MPC, fuzzy-MPC, and MPC–IL baselines, particularly during severe disturbances and uncertainty scenarios. Supplementary Material File (adil_ article.docx) Download 1.42 MB Information & Authors Information Version history V1 Version 1 04 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords frequency control fuzzy control iterative methods predictive control Authors Affiliations Adil Albarghooth 0009-0001-5527-7694 [email protected] Babol Noshirvani University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 149 views 68 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Adil Albarghooth. Improved Load-Frequency Control Using a Fuzzy Model Predictive Control Method Enhanced by Iterative Learning in Hybrid Power Systems. Authorea . 04 February 2026. DOI: https://doi.org/10.22541/au.177017774.46965826/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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