This study intends to address the platoon formation control problem of a team of N electrically driven underactuated autonomous car-like mobile robots. A platoon controller is proposed by using the relative distance and angle between each two successive robots in the platoon. A high-gain observer is also used to leave out velocity sensors to reduce the cost of the implementation/maintenance and the weight of the robots. Then, the dynamic surface control method is used to prevent complexity of the controller design. Next, by utilizing the prescribed performance bound method, predetermined desired transient and steady-state behavior of the tracking formation errors, robots connectivity preservation, and their collision avoidance are guaranteed as well as singularity avoidance. Adaptive neural networks and adaptive robust controllers are used to improve the tracking performance in the presence of parametric and nonparametric uncertainties, unmodeled dynamics, actuator saturation nonlinearity, and unwanted external disturbances including exogenous forces and torques, friction, and vibration. The Lyapunov direct method proves that all signals of the closed-loop control system are uniformly ultimately bounded. Finally, simulation results are demonstrated to show the superiority of the proposed convoy tracking system.

这项研究旨在解决N团队的排形成控制问题电驱动的欠驱动自动驾驶式移动机器人。通过使用排中每两个连续的机器人之间的相对距离和角度，提出了一种排控制器。高增益观察器还用于省去速度传感器，以减少实施/维护的成本以及机器人的重量。然后，使用动态表面控制方法来防止控制器设计的复杂性。接下来，通过使用规定的性能限制方法，可以确保跟踪形成误差的预定期望瞬态和稳态行为，机器人连接性的保持以及它们的避免碰撞以及避免奇点。自适应神经网络和自适应鲁棒控制器用于在存在参数和非参数不确定性，未建模的动力学，执行器饱和非线性以及不希望的外部干扰（包括外力和转矩，摩擦和振动）的情况下提高跟踪性能。李雅普诺夫直接法证明了闭环控制系统的所有信号最终都统一有界。最后，仿真结果证明了所提出的车队跟踪系统的优越性。