강화학습을 이용한 능동 현가장치 제어

Abstract

In recent years, research on machine learning techniques that can be integrated with existing suspension control algorithmsfor enhanced control effects has advanced considerably. Machine learning, especially involving neural networks, oftenrequires many samples, which makes maintaining robust performance in diverse, changing environments challenging. Thepresent study applied reinforcement learning, which can generalize complex situations not previously encountered, toovercome this obstacle and is crucial for suspension control under varying road conditions. The effectiveness of theproposed control method was evaluated on different road conditions using the quarter-vehicle model. The impact of trainingdata was assessed by comparing models trained under two distinct road conditions. In addition, a validation exercise onthe performance of the control method that utilizes reinforcement learning demonstrated its potential for enhancing theadaptability and efficiency of suspension systems under various road conditions.