Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm

Abstract

Boost converters play a crucial role in power electronics but present control challenges due to their non-minimum phase behavior and nonlinear dynamics at high switching frequencies. To address these issues, this work proposes a Fractional-order adaptive Model Predictive Control (FO-MPC) framework incorporating Exponential Regressive Least Squares (ERLS) for system identification. Traditional MPC frameworks often rely on accurate mathematical models, which are difficult to obtain in real-world scenarios. This adaptive modelling approach based on ERLS identification method eliminates the need for precise system models, improving robustness and adaptability under parameter variations. Additionally, a FO derivative term enhances damping, stability, and noise resistance, overcoming conventional MPC limitations. To optimize controller performance, Grey Wolf Optimization (GWO) is employed for fine-tuning FO-MPC parameters, ensuring improved tracking accuracy and disturbance rejection. The proposed FO-MPC is validated through simulations and experimental evaluations using an Arduino DUE-based setup. Comparative studies against PID and FO-PID controllers, also optimized with GWO, confirm the superior performance, stability, and adaptability of the FO-MPC, making it a practical and effective solution for high-performance Boost converter applications.