There is no doubt about significant roles of system models in control systems design. Besides, the current widespread scope of control engineering, ranging to extremely large-scale systems like social infrastructures, biology, humans and social networks, requires a highly-developed sense to regard such non-traditional systems as control systems. This course starts with introductions to fundamental tools for system modeling and how to use models in control systems design, and then presents a collections of examples spanning many different fields of science and engineering. This course also provides bases on an indispensable tool for system modeling, that is, discrete-time dynamical system theory. The focus of the course is then shifted to system identification, a methodology for system modeling, where ARX model-based and state-space identification techniques are introduced. Finally, students would have an opportunity for learning how to use MATLAB System Identification Toolbox.
This course educates the concept of system modeling, modeling examples, the role of models in the control system design, modeling methodologies including how to use software. A goal of this course is that students will be able to model a variety of systems as control systems beyond traditional ones like mechanical and electrical systems. This would have students recognize that control engineering can broadly contribute to the society. On the other hand, by educating the way to use modeling software, students will eventually acquire a skill to build a system model.
System, Modeling, Dynamics, System Identification, System Identification Toolbox
|✔ Specialist skills||Intercultural skills||Communication skills||Critical thinking skills||Practical and/or problem-solving skills|
1) At the beginning of each class, solutions to exercise problems assigned during the previous class are reviewed.
2) Attendance is taken in every class.
3) Students must familiarize the contents assigned in the previous class before coming to each class.
|Course schedule||Required learning|
|Class 1||System Modeling and Fundamental Tools||Students must make sure they understand what significance the course holds for them by checking their learning portfolio. Students must be able to explain the concept of system modeling and the tools for modeling.|
|Class 2||System Models in Control System Design||Students must be able to explain how to use system models in control system design.|
|Class 3||Discrete-time Dynamical Systems||Students must be able to explain descriptions of discrete-time dynamical systems in time domains and fundamentals of its stability.|
|Class 4||Discrete-time Signals and z-Transforms||Students must be able to explain representations of discrete-time signals and z-transforms.|
|Class 5||Pulse Transfer Function and Its Stability||Students must be able to explain descriptions of discrete-time dynamical systems in frequency domains and fundamentals of its stability.|
|Class 6||Frequency Characteristics of Discrete-time Systems||Students must be able to explain frequency characteristics of discrete-time dynamical systems.|
|Class 7||Modeling Example(1) - Mechanical Systems||Students must be able to view a variety of products and phenomena as a system through learning of modeling examples for mechanical systems.|
|Class 8||Modeling Example(2) - Electrical and ICT Systems||Students must be able to view a variety of products and phenomena as a system through learning of modeling examples for electrical and ICT systems.|
|Class 9||Modeling Example(3) - Biological, Psychological and Societal Systems||Students must be able to view a variety of products and phenomena as a system through learning of modeling examples for biological, psychological and societal systems.|
|Class 10||Fundamentals on Random Process||Students must be able to explain fundamentals on random process.|
|Class 11||System Identification and Its Procedure||Students must be able to explain the concept of system identification and its procedure.|
|Class 12||Identification of Non-parametric Models||Students learn how to identify non-parametric models.|
|Class 13||Identification of ARX Models||Students must know the representation of ARX models and be able to identify its parameters.|
|Class 14||State Space Identification||Students must be able to explaning the concept and techniques of state space identification.|
|Class 15||How to Use SysID Toolbox||Student must be able to execute identification using GUI of MATLAB System Identification Toolbox.|
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Adachi, Shuichi. ISBN 9784501114800. (Japanese)
Karl J. Astrom and Richard M. Murray. Feedback Systems. Princeton University Press, ISBN: 0691135762
Y. Matsuo, Digital Control, Shokodo, ISBN: 4785690593
Students will be assessed on their understanding of the concept of system modeling, solution and their applications. The course scores are based on mid-term exam (50%) and reports (50%).
Students must have successfully completed SCE.C.201, SCE.C.202, SCE.C.301, and SCE.E.202 or have equivalent knowledge.