Reinforcement learning adaptive fuzzy controller for lighting systems: application to aircraft cabin

TL;DR

This paper presents an adaptive lighting control system combining fuzzy logic and reinforcement learning to personalize lighting in aircraft cabins, effectively learning user preferences and adapting to environmental conditions.

Contribution

It introduces a novel reinforcement learning-based tuning of fuzzy inference systems for personalized lighting control in aircraft cabins, addressing subjective user preferences.

Findings

The algorithm learns user preferences through feedback corrections.

It adapts effectively to diverse environmental and user conditions.

The system demonstrates successful real-world implementation in a mockup.

Abstract

The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge. We use the existing literature to create a FIS that generates lighting setting recommendations based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning agent, which tunes the FIS parameters online to match the user preferences. We implement the algorithm in an aircraft cabin mockup and conduct an extensive user study to evaluate the effectiveness of the algorithm and understand its learning behavior. Our implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. and can deal with a diverse spectrum of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities.