Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter

Abstract

DC/ DC Boost converter has a right half-plane zero structure called a non-minimum phase system, which presents several challenging constraints for designing well-behaved control techniques. The Fractional-Order concept as a beneficial scheme provides several advantages, such as lower sensitivity to noise and parametric variation. For this purpose, a Fractional-order Proportional-Integrated-Derivative (FOPID) controller is designed for the Boost converter. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. An Artificial Neural Network structure (ANNs) is adopted here to optimize the gains of the FOPID in challenging conditions. This method suffers from higher complexity and slower dynamics in practical applications. A single-layer ANNs is designed to reduce the complexity; also, particle swarm optimization (PSO) algorithm is utilized in this decision-making structure to provide better results with its online mechanism. Furthermore, the Black-box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. The Artificial Neural Network structure can optimize the FOPID gains real-time, even in severe challenging conditions, efficiently. To better examine the superiority of the proposed method, conventional Proportional-Integrated-Derivative (PID) and the FOPID controllers are proposed to drive a comparison with this work, which are tuned by the PSO optimization algorithm. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving stability but also for compensating for the disturbances and uncertainties in the Boost converter, properly. JEL CLASSIFICATIONEnergy engineering: Power electronics, electric power applications INTRODUCTIONAccording to the basic principle, DC/DC power converters, since they can regulate the output voltage with the minimumThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

“…In addition, adaptive-based approaches are proposed with higher efficiency in challenging cases considering their flexibility and better outcomes. Some of the most recent adaptive controllers used for power converters are listed here: neural network-based adaptive [22][23][24][25], adaptive predictive [26][27][28][29], optimized adaptive sliding mode [30], and Lyaponuv-based adaptive [31,32] strategies. The main benefits presented by these approaches are effectiveness in ill-defined models, higher robustness in uncertainties, faster dynamical operation, and better external disturbance rejection.…”