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.

翻译：本文提出了一种新颖的基于模型的自适应共享控制方法，以应对共享控制系统中人类与自动化系统分担控制任务时的辨识与设计挑战。该方案的核心难点在于人类在共享控制交互中的自适应行为。因此，仅辨识人类行为而不考虑自动化系统特性往往会导致自动化设计不充分。为此，本文提出了一种创新解决方案，即通过线性-二次型微分博弈实现在线辨识人类行为并自适应调整共享控制策略。通过仿真分析验证了所提在线自适应方法的有效性，并与现有非自适应共享控制方法进行了对比。最后，通过包含人类操作者的在环实验验证了该方法在实时应用中的适用性。