Summary of 1.1 Modeling and simulation of dynamical systems (AE3B35MSD): Terminology, motivation, scope
The video introduces modeling and simulation of dynamical systems, covering terminology, motivation, and scope. A system is defined as a subset of physical reality with specific boundaries and variables, while a dynamical system responds to changes in time through mathematical equations, usually differential equations. The importance of modeling in control design is emphasized, with benefits such as insight development, design suggestions, model-based control design, and simulation-based verification. The course focuses on systems where energy is applicable, enabling modeling of multi-domain and complex systems by decomposing them into subsystems and components. Three major modeling approaches are discussed: graphical (power bond graphs), analytical (Lagrange methodology), and software-based (Modelica and SimScape). System identification is excluded from the course due to the need for a background in probability and statistics and a different philosophy.
### Methodology
- Define systems with boundaries and physical variables of interest
- Define dynamical systems as responding to changes in time with modeling through mathematical equations
- Utilize modeling for control design stages: insight development, design suggestions, model-based control design, simulation-based verification
- Consider systems where energy makes sense for modeling multi-domain and complex systems
- Use graphical, analytical, and software-based modeling approaches (power bond graphs, Lagrange methodology, object-oriented programming patterns)
- Exclude system identification due to required background and different philosophy
### Speakers
- Unnamed speaker
Notable Quotes
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07:56
— « Simulation it's nothing else for us then experimentation with the model.
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08:40
— « Our ultimate motivation for studying and for pursuing modeling is to assist in control design.
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14:25
— « It's really good to keep this in mind because it's the only way to avoid whatever overkills, missteps, and later frustration.
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17:22
— « It will allow us to apply hierarchical or modular or component-based approach to modeling.
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21:03
— « Our motivation for modeling is to get physical insight into the physics of the problem. »