Fuzzy Finite-Time Pose Tracking Control With Nonlinear Output Scale Factor for a Four Mecanum-Wheel Vehicle in Smooth Indoor and Complex Outdoor Environments
针对四麦克纳姆轮车辆在室内外不同地形下的位姿跟踪问题，提出一种结合边缘计算的模糊有限时间控制方法，通过非线性输出比例因子应对不确定性和位姿不连续性，仿真与实验验证了其鲁棒性优于现有方法。
It is known that the advantageous feature of Mecanum wheel vehicle (MWV) is its perfect trajectory tracking in a narrow space. For an AI platform, appropriate sensors, e.g., vision and LiDAR, are installed along the planned head direction to obtain human/object recognition tasks. However, most works of MWV only discussed their trajectory tracking in an indoor congested environment. In this article, a mathematical model including kinematics, dynamics with friction, and motor dynamics of a four Mecanum-wheel vehicle (FMWV) for smooth indoor ground or complex outdoor asphalt, bumpy, and uneven surface roads is first derived. To accomplish pose tracking tasks of sensor-based FMWV under different terrain and uncertain conditions, the more efficient and resilient fuzzy finite-time pose tracking control (FFTPTC) using edge computing is proposed. The FFTPTC comprises two portions: 1) a nominal control based on the derived model to achieve the planned tasks and 2) fuzzy enhanced control (FEC) with a new nonlinear output scale factor (NOSF) to contend with lumped uncertainties and the discontinuity of the desired and initial poses. Not only is robust stability improved but also are robust pose tracking simulation and experiment performances obtained in comparison to the state-of-the art methods.