School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford, MK43 0AL, UK.
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford, MK43 0AL, UK.
ISA Trans. 2023 Jun;137:646-655. doi: 10.1016/j.isatra.2022.12.011. Epub 2022 Dec 16.
Risk mitigation is usually addressed in simulated environments for safety critical control. The migration of the final controller requires further adjustments due to the simulation assumptions and constraints. This paper presents the design of an experience inference algorithm for safety critical control of unknown multi-agent linear systems. The approach is inspired in the close relationship between three main areas of the brain cortex that enables transfer learning and decision making: the hippocampus, the neocortex, and the striatum. The hippocampus is modelled as a stable linear model that communicates to the striatum how the real-world system is expected to behave. The hippocampus model is controlled by an adaptive dynamic programming (ADP) algorithm to achieve an optimal desired performance. The neocortex and the striatum are designed simultaneously by an actor control policy algorithm that ensures experience inference to the real-world system. Experimental and simulations studies are carried out to verify the proposed approach.
风险缓解通常在安全关键控制的模拟环境中进行。由于模拟假设和约束，最终控制器的迁移需要进一步调整。本文提出了一种用于未知多智能体线性系统安全关键控制的经验推理算法的设计。该方法的灵感来自于大脑皮层三个主要区域之间的密切关系，这些区域可以实现迁移学习和决策：海马体、新皮层和纹状体。海马体被建模为一个稳定的线性模型，它向纹状体传达了对现实世界系统行为的预期。海马体模型由自适应动态规划（ADP）算法控制，以实现最优的期望性能。新皮层和纹状体通过一个演员控制策略算法同时设计，该算法确保了对现实世界系统的经验推理。进行了实验和模拟研究以验证所提出的方法。
