Path-following control of autonomous ground vehicles based on input convex neural networks. (November 2022)

• Record Type: Journal Article
• Authors: Jiang, Kai; Hu, Chuan; Yan, Fengjun
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 13(2022)
• Page Start: 2806
• Page End: 2816
• Publication Date: 2022-11
• Subjects: Autonomous ground vehicles -- path following -- predictive control -- neural network; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/09544070221114690
• Languages: English
• ISSNs: 0954-4070

Abstract

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.