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Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Mahmoud Elsisi, Minh‐Quang Tran, Hany M. Hasanien, Rania A. Turky, Fahad Albalawi, Sherif S. M. Ghoneim

2021Mathematics85 citationsDOIOpen Access PDF

Abstract

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

Topics & Concepts

Control theory (sociology)Robustness (evolution)Model predictive controlSettling timeVoltage regulatorOvershoot (microwave communication)Computer scienceVoltageStep responseStability (learning theory)Firefly algorithmAlgorithmControl engineeringEngineeringControl (management)Machine learningArtificial intelligenceBiochemistryElectrical engineeringParticle swarm optimizationTelecommunicationsChemistryGeneAdvanced Control Systems OptimizationAdvanced Control Systems DesignControl Systems and Identification
Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms | Litcius