A Double Integral Fuzzy Zeroing Neural Dynamics Controller and Its Application in Quadrotor UAV Trajectory Tracking
提出一种双积分模糊归零神经动力学控制器，通过自适应参数调整增强四旋翼无人机在无界扰动下的轨迹跟踪鲁棒性，理论验证全局收敛性，仿真显示均方根误差比对比方法降低84.61%和48.35%。
Quadrotor unmanned aerial vehicles (UAVs) have attracted substantial attention due to their simple structure and strong adaptability, but enhancing robustness and adaptability for trajectory tracking under unbounded disturbances remains a key challenge. To address this, this article proposes a novel double integral fuzzy zeroing neural dynamics controller (DIFZNDC). The DIFZNDC integrates a double integral neural dynamics model with a novel dual-input single-output fuzzy logic system (DISOFLS), which can adaptively adjust the parameters, thereby enhancing the robustness and adaptability of the controller. In addition, the global convergence and robustness of the system under the DIFZNDC are theoretically verified. Moreover, two trajectory tracking examples demonstrate the effectiveness and superiority of the DIFZNDC for the quadrotor system. Quantitative analysis under Gaussian disturbance indicates that the DIFZNDC reduces the root-mean-square error (RMSE) by 84.61% and 48.35% compared to the modified super-twisting controller (MSTC) and the fixed-time zeroing neural dynamics controller (FTZNDC), respectively.