Now that ubiquitous society has come, computerized/intelligent control systems are achievable everywhere. Our laboratory is working on system control, machine learning, signal processing, and interdisciplinary study based on these topics. In what follows, after stating general overview, we show our current research topics. Please note that these are just examples and we always challenge a new topic.
As the term “control” is used in our daily life, this concept is familiar and basic. Control technology is widely used in industry, but at this moment only simple control laws are adopted in practice and higher performance is strongly desired. This can be achieved by modeling dynamics of the object and analyzing/designing the system in an intelligent way. It is also desirable to build a learning mechanism as a human shows improvement with his/her progress. It is thus our target to construct a highly intelligent control system. Because of its high level of abstraction, you might feel that it is not practical right now, but the knowledge you learn here will be surely useful when you enter the real world.
Summaries of topics conducted by students are here!
Ongoing research topics (examples)
Robust/Adaptive learning control
We are studying advanced control technology such as robust control and its application. For example we are currently studying Feedback Error Learning (FEL) a famous biological model of motion learning, from a control-theoretic viewpoint. We are also interested in data-driven control systems by making full use of computers.
Networked control/Switched system
We consider how control technology should be, in this era of Iot where everything is connected to the Internet. Specifically we are proposing a switched control system that is robust against random delay of signal transmission (jitter) or apriodic sampling (packet loss), and verifying their effectiveness via experiment with small vehicles such as drone.
Distributed optimization/agreement algorithm
In order to handle big data or privacy issues, we are considering distributed algorithms for solving optimization/agreement problems. We are also conducting its theoretical and numerical analysis.
Networks are now appearing everywhere in society. We are therefore conducting qualitative and quantitative analysis of network features using graph theory and constraint satisfaction problems. We are also applying it to power system analysis, infectious disease models, and SNS information diffusion models.