# Reservoir computing-driven inverse dynamics for autonomous vehicle trajectory tracking control

## TL;DR

This paper introduces a real-time control method using reservoir computing to improve autonomous vehicle trajectory tracking under complex conditions.

## Contribution

A novel real-time optimal control method using reservoir computing for autonomous vehicle inverse dynamics with online PD feedback correction.

## Key findings

- The method achieves high-precision trajectory tracking with computational efficiency.
- It shows robustness against control disturbances and sensor noise.
- Moderate sensor noise can enhance performance depending on the trajectory.

## Abstract

The strong coupling between lateral and longitudinal dynamics in autonomous vehicles presents a significant challenge for trajectory tracking control, especially under high-dynamic and complex conditions. To address this, this paper proposes a real-time optimal control method driven by a Reservoir Computing (RC)-based vehicle inverse dynamics model. The approach first involves training an RC network on a comprehensive vehicle dynamics dataset, covering multiple operating conditions, to learn the inverse mapping from accelerations to control commands. Second, an online correction mechanism incorporating Proportional-Derivative (PD) feedback is designed to dynamically adjust the desired acceleration inputs based on trajectory tracking errors. Finally, these corrected accelerations are fed into the trained RC network to rapidly compute high-precision control commands, completing the closed-loop tracking. Comprehensive simulations on double-lane-change, figure-eight, and Rössler chaotic trajectories demonstrate that the proposed method achieves high-precision tracking with remarkable computational efficiency and excellent robustness against control disturbances and sensor noise. Notably, moderate sensor noise exhibits trajectory-dependent performance enhancement, with system failure boundaries under combined disturbances clearly characterized.