APPLICATION OF DEEP REINFORCEMENT LEARNING INSEMI-ACTIVE CONTROL OF FRAME STRUCTURESEQUIPPED WITH MULTIPLE DAMPER DEVICES

In semi-active control of structures, control algorithms determine the adjustable reaction ofdampers to mitigate the seismic response of the structure. Existing control algorithms typically followa set of predefined rules. Alternatively, data-driven methods can learn from computer simulations toidentify patterns and to make adaptive decisions. Deep reinforcement learning is a subset of machinelearning that utilizes neural networks to perform optimal actions in dynamic environments. In thisstudy, the seismic response of a ۲D, ۹-story frame structure equipped with multiple semi-activemagnetorheological (MR) dampers was controlled using a Twin-Delayed Deep Deterministic PolicyGradient (TD۳) reinforcement learning model that utilized a novel reward system. To assess thesuperiority of the method, the performance of the TD۳ model was compared with that of the skyhookcontrol algorithm.

کلیدواژه ها:

Seismic Response Mitigation ، Semi-Active Control ، Reinforcement Learning ، IntelligentControl ، Magnetorheological Damper