| Budget Amount *help |
¥43,420,000 (Direct Cost: ¥33,400,000、Indirect Cost: ¥10,020,000)
Fiscal Year 2019: ¥9,100,000 (Direct Cost: ¥7,000,000、Indirect Cost: ¥2,100,000)
Fiscal Year 2018: ¥8,970,000 (Direct Cost: ¥6,900,000、Indirect Cost: ¥2,070,000)
Fiscal Year 2017: ¥9,230,000 (Direct Cost: ¥7,100,000、Indirect Cost: ¥2,130,000)
Fiscal Year 2016: ¥8,320,000 (Direct Cost: ¥6,400,000、Indirect Cost: ¥1,920,000)
Fiscal Year 2015: ¥7,800,000 (Direct Cost: ¥6,000,000、Indirect Cost: ¥1,800,000)
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| Outline of Final Research Achievements |
Regarding real-time algorithms of nonlinear model predictive control (NMPC), we succeeded in improving the efficiency by parallel computing and by exploiting structures of problems such as systems including discontinuities, nonlinear partial differential equations, and multi-link systems. For the algebraic approach, we obtained a new method for the maximum posterior probability estimation problem, new optimality conditions for constrained optimization problems, and an analysis method for Markov processes by the holonomic gradient method. In terms of applications, we demonstrated the effectiveness of NMPC in systems such as heat conduction systems, robot arms, fault-tolerant control, target tracking and obstacle avoidance of drones, climbing control of humanoid robots, and blade-pitch control of floating offshore wind turbines.
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