Continuous-time finite-horizon ADP for automated vehicle controller design with high efficiency

TL;DR

This paper introduces a continuous-time finite-horizon ADP method for automated vehicle control that efficiently synthesizes near-optimal policies with high accuracy and significantly reduced computation time, suitable for nonlinear systems.

Contribution

It proposes a novel ADP approach using neural networks for vehicle control, achieving fast convergence and high accuracy in near-optimal policy synthesis.

Findings

Achieves 1% error in control policy.

Reduces computation time by nearly 500 times compared to nonlinear MPC.

Successfully applied to automated vehicle path tracking.

Abstract

The design of an automated vehicle controller can be generally formulated into an optimal control problem. This paper proposes a continuous-time finite-horizon approximate dynamicprogramming (ADP) method, which can synthesis off-line near-optimal control policy with analytical vehicle dynamics. Lying on the general Policy Iteration framework, it employs value andpolicy neural networks to approximate the mappings from thesystem states to value function and control inputs, respectively. The proposed method can converge to the near-optimal solutionof the finite-horizon Hamilton-Jacobi-Bellman (HJB) equation. We further applied our algorithm to the simulation of automated vehicle control for the path tracking maneuver. The results suggest that the proposed ADP method can obtain the near-optimal policy with 1% error and less calculation time. What is more, the proposed ADP algorithm is also suitable for nonlinear control systems, where ADP is almost 500 times faster than the nonlinear MPC ipopt solver.