This paper studies the path-following problems in autonomous ground vehicles (AGVs) through predictive control and neural network modeling. Considering the model of AGVs is usually difficult to construct by first principles accurately, a data-driven approach based on deep neural networks is proposed to deal with the system identification tasks. Although deep neural networks have good representation capability for complex system, they are still hard to use for control area due to their nonconvexities and nonlinearities. Therefore, to make a trade-off between control tractability and model accuracy, the input convex neural networks (ICNNs) are developed to describe the dynamics of AGVs. As the designed neural networks are convex with regard to the inputs, the predictive control problem is converted to a convex optimization problem and thus it’s easier to get feasible solutions. Besides, for adapting to different road conditions and some other disturbances, a periodically online learning algorithm is designed to update the neural network. Finally, two driving simulations under CarSim-Simulink platform are conducted to prove the superiority of our proposed techniques.

本文通过预测控制和神经网络建模研究了自主地面车辆 (AGV) 中的路径跟踪问题。考虑到 AGV 模型通常难以通过第一原理准确构建，提出了一种基于深度神经网络的数据驱动方法来处理系统识别任务。尽管深度神经网络对复杂系统具有良好的表示能力，但由于其非凸性和非线性，它们仍然难以用于控制区域。因此，为了在控制易处理性和模型精度之间进行权衡，开发了输入凸神经网络 (ICNN) 来描述 AGV 的动力学。由于设计的神经网络在输入方面是凸的，预测控制问题被转换为凸优化问题，因此更容易获得可行的解决方案。此外，为了适应不同的路况和其他一些干扰，设计了一种周期性的在线学习算法来更新神经网络。最后，在 CarSim-Simulink 平台下进行了两次驾驶模拟，以证明我们提出的技术的优越性。