A Novel Fractional-Order Adaptive Gain Fixed-Time Convergent Super-Twisting-Algorithm

This paper presents a novel fractional-order adaptive gain fixed-time convergent super-twisting-algorithm (FOAFSTA). The proposed FOAFSTA has the property of fixed-time convergence. Compared with other Super-Twisting-Algorithm-like algorithms, it still has faster convergence speed and stronger robustness even under a larger disturbance. The new adaptive law can reduce the overestimation of gain. Based on Lyapunov theory, the convergence time of the proposed FOAFSTA is estimated. The corresponding comparative simulation demonstrates the effectiveness and superiority of the proposed FOAFSTA.