Adaptive Interval Type-2 Fuzzy Neural Network-Based Novel Fixed-Time Backstepping Control for Uncertain Euler-Lagrange Systems.

In this article, a novel adaptive fixed-time fuzzy control algorithm is designed for uncertain Euler-Lagrange systems with actuator control input saturation. In contrast to existing algorithms, this paper explores a faster fixed-time backstepping control algorithm. It enables the system to achieve fixed-time convergence with a faster convergence rate and obtain a smaller upper bound of the convergence time. To address the problem of actuator control input saturation, a novel fixed-time auxiliary system is constructed, involving coordinate transformation of the system's error variables to mitigate the effects of saturation. In response to the unknown dynamics (including model uncertainty, external disturbance, etc.) of the Euler-Lagrange system, this paper designs an adaptive interval type-2 fuzzy neural network for estimation and compensation. Stability analysis confirms that the tracking error can achieve faster fixed-time convergence. Simulation and experimental results demonstrate that the proposed control algorithm can enhance dynamic and steady-state tracking control performance.