Design of a novel robust type‐2 fuzzy‐based adaptive backstepping controller optimized with antlion algorithm for buck converter

Abstract

A type‐2 fuzzy logic‐based adaptive backstepping control (T2F‐ABSC) approach is presented for a DC/DC Buck converter. Lyapunov‐based backstepping control (BSC), which can guarantee convergence along with asymptotic stability of the system. Also, 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. 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. Type‐2 fuzzy structure is adopted here to optimize the gains of the ABSM in challenging conditions. Type‐2 fuzzy (T2F) has higher efficiency and faster dynamics with more adaptability to the system. To enhance the performance of the T2F, antlion optimization (ALO) has been used in this structure. ALO is a modern nature‐inspired algorithm, and it has some advantages over other optimizing algorithms. Furthermore, it has ability to find optimal answers in a shorter time, more accurately in contrast to the other optimization algorithms and is used to tune the membership function parameters of the (T2F) under the diverse search spaces. To depict the strength of the designed work, fuzzy‐PID and backstepping schemes proposed to carry out an analysis with this work.

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

“…The capacity to find ideal gains for power converters is very strong in metaheuristic bio-inspired algorithms. Several swarm intelligence optimization algorithms have been proposed for optimizing fuzzy parameters, including the Cat Swarm Algorithm (CSA) [79], Gray Wolf Optimization (GWO) [80], Bat Algorithm (BA) [81], Firefly Algorithm (FA) [82], Artificial Bee Colony (ABC) [83], Antlion Optimization (ALO) [84] and Whale Optimization Algorithm (WOA) [85]. The ALO is a highly influential metaheuristic algorithm that has garnered significant interest.…”