| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Outline of Final Research Achievements |
In this study, we have developed the latest neural network structure-based controllers and their data-driven tuning methods by applying various deep learning techniques in order to improve their practicality and versatility. We have also developed data-driven control system design methods without a reference model by applying deep reinforcement learning. In particular, we have improved response prediction methods so that control performance evaluation is accelerated in the control system design process. Furthermore, we have proposed a method for constructing an evaluation model using a neural network that can represent the control engineer's knowledge and insight for control characteristics.
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