Design of a novel robust adaptive cascade controller for DC‐DC buck‐boost converter optimized with neural network and fractional‐order PID strategies

Abstract A cascade technique with two control loops is designed for a DC \ Buck‐Boost converter that right half‐plane zero (RHPZ) structure called non‐minimum phase system. This concept presents several challenging constraints designing well‐behaved techniques. Cascade controllers can provide various benefits compared single loop such as higher safety, robustness, and stability. strategy assumes the system black‐box without need mathematical model of benefit decrease computational burden provides faster dynamics along ease implementation. consisted an outer Fractional‐order PID voltage controller tuned Antlion Optimizer (ALO) algorithm, which reference current inner Neural Network‐based LQR (NN‐LQR) controller. The basic principle in more rapid performance has been satisfied NN‐LQR strategy, optimizes tunes gains shows robustness. It should be mentioned number neurons limited to 2 4 each layer lower complexity. Also, ALO algorithm modern nature‐inspired used tune better results under‐constrained problems diverse search spaces. Considering negative impacts disturbances on power converter, Fractional‐order‐based (FO‐PID) proper alternative since it robustness load uncertainties dynamical responses based its extra degree freedom. Moreover, evaluate superiority this controller, other are using PSO FO‐PID controllers. Finally, presented tested working conditions through simulation experiment results.