Composite Nonlinear Trajectory Tracking Control of Co-Driving Vehicles Using Self-Triggered Adaptive Dynamic Programming

TL;DR

The paper addresses safe and efficient lateral control in human–machine shared driving by developing a composite nonlinear feedback controller integrated with a self-triggered adaptive dynamic programming routine. The approach combines a 2-DOF vehicle model, a two-point preview driver model, and a dynamic authority allocation mechanism to fuse human and automated inputs, while leveraging CNF to enhance transient response and ST-ADP to enable model-free, data-driven optimization. Stability is established for both event- and self-triggered implementations, and the ADP algorithm iteratively learns the optimal policy (K, P) from measured data, with the final policy combining a linear state feedback, a curvature term, and a nonlinear damping term. Carsim-Simulink co-simulations across simple and complex road geometries demonstrate reduced computation via self-triggering and improved tracking, validating the practical potential of cooperative steering control. The work offers a principled, data-driven pathway to robust, low-overhead shared steering in evolving automated driving systems.

Abstract

This article presents a composite nonlinear feedback (CNF) control method using self-triggered (ST) adaptive dynamic programming (ADP) algorithm in a human-machine shared steering framework. For the overall system dynamics, a two-degrees-of-freedom (2-DOF) vehicle model is established and a two-point preview driver model is adopted. A dynamic authority allocation strategy based on cooperation level is proposed to combine the steering input of the human driver and the automatic controller. To make further improvements in the controller design, three main contributions are put forward. Firstly, the CNF controller is designed for trajectory tracking control with refined transient performance. Besides, the self-triggered rule is applied such that the system will update in discrete times to save computing resources and increase efficiency. Moreover, by introducing the data-based ADP algorithm, the optimal control problem can be solved through iteration using system input and output information, reducing the need for accurate knowledge of system dynamics. The effectiveness of the proposed control method is validated through Carsim-Simulink co-simulations in diverse driving scenarios.

Figures (15)

• Figure 1: General framework of the co-driving system.
• Figure 2: Vehicle dynamic model.
• Figure 3: Human driver steering principle.
• Figure 4: Composition of human driver model.
• Figure 5: Convergence of matrix $P$ and feedback gain $K$.