Design of a novel robust adaptive backstepping controller optimized by snake algorithm for buck-boost converter

Abstract

A power DC-DC Buck-Boost converter is controlled using a Lyapunov-based Adaptive Backstepping Control (ABSC) technique. It exhibits unfavorable behavior due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. This strategy is an enhanced iteration of the technique that uses the stability Lyapunov function to achieve greater stability and improved resistance to disturbances in real-world scenarios. Furthermore, the Black-box technique is employed to minimize the computing workload and facilitate implementation, under the assumption that there is no precise mathematical model available for the system. However, in real-time settings, disruptions with broader scopes such as fluctuations in supply voltage, variations in parameters, and noise might have adverse effects on the functioning of this approach. There is a need to set the most suitable initial gains for the controller to enhance its flexibility in more challenging working conditions. Therefore, to meet this requirement and enhance the effectiveness of the controller, the control scheme integrates a computational method called the Snake optimization (SO) algorithm. The SO method is known for its disciplined and nature-inspired approach, which results in faster decision-making and greater accuracy compared to other optimization algorithms. In order to further explain the advantages of this method, classical Backstepping and SO-based PID schemes are also developed and evaluated in various scenarios. The effectiveness of this approach is tested in both simulation and experimental environments, showing significant outcomes and lower sensitivity to error.