Data-based reinforcement learning for lane keeping with input saturation. (15th September 2020)

• Record Type: Journal Article
• Authors: Luo, Rui; Qian, Dianwei; Zhang, Qichao
• Is Part Of: International journal of advanced mechatronic systems. Volume 8:Number 1(2020)
• Page Start: 9
• Page End: 15
• Publication Date: 2020-09-15
• Subjects: lateral control -- lane keeping -- input saturation -- model-free reinforcement learning; Mechatronics -- Periodicals; 629.89
• Languages: English
• ISSNs: 1756-8412

Abstract

With the development of artificial intelligence, autonomous driving has received extensive attention. As a very complex integrated system, the autonomous vehicle has several modules. This paper is related to the control module, which is used to design an optimal or near-optimal controller to control the desired trajectory of the vehicle. In this paper, lateral control strategy for lane keeping task is proposed based on the model-free reinforcement learning. Different from the model-based methods such as linear quadratic regulator and model predictive control, our method only requires the generated data rather than the perfect knowledge of the system model to guarantee the optimal performance. At the same time, in order to meet two needs of passengers' comfort and fuel economy, input saturation should be considered in the design of the control module. A low-gain state feedback control method is adopted. It mainly solves some algebraic Riccati equations for data-based lateral control. Finally, the corresponding simulation is given and the validity of the algorithm is verified.