Incorporating ESO into Deep Koopman Operator Modelling for Control of Autonomous Vehicles

TL;DR

This paper introduces a deep Koopman operator-based model with an extended state observer for improved control of autonomous vehicles, enhancing trajectory tracking accuracy in complex scenarios.

Contribution

It develops a deep neural network approach to approximate the Koopman operator, integrates ESO for disturbance estimation, and applies this to model predictive control for autonomous vehicle trajectory tracking.

Findings

Outperforms linear and nonlinear MPC in tracking accuracy.

Effectively estimates disturbances online with ESO.

Improves model generalization and robustness.

Abstract

Koopman operator theory is a kind of data-driven modelling approach that accurately captures the nonlinearities of mechatronic systems such as vehicles against physics-based methods. However, the infinite-dimensional Koopman operator is impossible to implement in real-world applications. To approximate the infinite-dimensional Koopman operator through collection dataset rather than manual trial and error, we adopt deep neural networks (DNNs) to extract basis functions by offline training and map the nonlinearities of vehicle planar dynamics into a linear form in the lifted space. Besides, the effects of the dimensions of basis functions on the model accuracy are explored. Further, the extended state observer (ESO) is introduced to online estimate the total disturbance in the lifted space and compensate for the modelling errors and residuals of the learned deep Koopman operator (DK) while also improving its generalization. Then, the proposed model is applied to predict vehicle states within prediction horizons and later formulates the constrained finite-time optimization problem of model predictive control (MPC), i.e., ESO-DKMPC. In terms of the trajectory tracking of autonomous vehicles, the ESO-DKMPC generates the wheel steering angle to govern lateral motions based on the decoupling control structure. The various conditions under the double-lane change scenarios are built on the CarSim/Simulink co-simulation platform, and extensive comparisons are conducted with the linear MPC (LMPC) and nonlinear MPC (NMPC) informed by the physics-based model. The results indicate that the proposed ESO-DKMPC has better tracking performance and moderate efficacy both within linear and nonlinear regions.