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RLS 기반의 Natural Actor-Critic 알고리즘을 이용한 터널 환기제어기 설계
주백석(B. Chu),김동남(D. Kim),홍대희(D. Hong),박주영(J. Park),정진택(J. T. Chung),김태형(T.-H. Kim) 한국정밀공학회 2006 한국정밀공학회 학술발표대회 논문집 Vol.2006 No.5월
The main purpose of tunnel ventilation system is to maintain CO pollutant and VI (visibility index) under an adequate level to provide drivers with safe driving condition. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. Constructing the reward of the tunnel ventilation system, two objectives listed above are included. RL algorithm based on actor-critic architecture and natural gradient method is adopted to the system. Also, the recursive least-squares (RLS) is employed to the learning process to improve the efficiency of the use of data. The simulation results performed with real data collected from existing tunnel are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.