Toward Adaptive Cooperation: Model-Based Shared Control Using LQ-Differential Games

TL;DR

This paper presents a new model-based shared control method that adaptively identifies human behavior and adjusts automation in real-time using Linear-Quadratic differential games, improving shared control systems.

Contribution

It introduces an online adaptive shared control framework combining human identification and control adaptation via LQ-differential games, addressing the challenge of human variability.

Findings

The proposed method outperforms non-adaptive control in simulations.

Real-time human-in-the-loop experiments validate the approach.

Adaptive control improves cooperation efficiency.

Abstract

This paper introduces a novel model-based adaptive shared control to allow for the identification and design challenge for shared-control systems, in which humans and automation share control tasks. The main challenge is the adaptive behavior of the human in such shared control interactions. Consequently, merely identifying human behavior without considering automation is insufficient and often leads to inadequate automation design. Therefore, this paper proposes a novel solution involving online identification of the human and the adaptation of shared control using Linear-Quadratic differential games. The effectiveness of the proposed online adaptation is analyzed in simulations and compared with a non-adaptive shared control from the state of the art. Finally, the proposed approach is tested through human-in-the-loop experiments, highlighting its suitability for real-time applications.