An adaptive fixed-time robust controller based on neural dynamics for time-varying tasks of quadrotors

Taking inspiration from zeroing neural dynamics with fixed-time convergence and robustness, this paper proposes an adaptive fixed-time robust controller (AFTRC) for tracking time-varying tasks of quadrotors. In the construction of the AFTRC, a new adaptive parameter based on system error is introduced to achieve higher convergence factors in case of large errors. Besides, two fixed-time activation functions are utilized in the AFTRC to achieve fixed-time convergence. Later, we introduce a design formula with integral control terms to enhance the robustness of AFTRC. The theoretical analysis demonstrates that the quadrotor system utilizing AFTRC achieves fixed-time convergence and has strong robustness against bounded disturbances. Moreover, numerical simulation results strictly confirm the theoretical analysis and demonstrate the effectiveness and superiority of AFTRC.