| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
As a basis of a new control system design method with a correction function based on computational intelligence mainly for deep learning, we extended the control method combining PID control and sliding mode control with model predictive control. A linear multiple regression model constructed from the results of analyzing a large amount of video data and a control method based on the inverse problem of optimal control based on this model are also developed. Furthermore, a motion control method for an autonomous vehicle that extends the potential function into space and time and calculates the predicted trajectory of obstacles based on data and learning is developed. The practicality of these development methods was verified for large-scale complex systems such as smart grids and next-generation urban transportation systems in pedestrian coexistence spaces.
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