Modeling Dynamic Systems with MATLAB and Simulink

With Model-Based Design, you use virtual models to design, implement, and deliver complex engineered systems. Virtual models that capture the dynamics of your physical system help test and prove your algorithms before implementing them on your hardware, thereby helping to identify problems early in the development cycle. Depending on users' understanding of the physical laws that govern the system, there is a spectrum of options for creating these models ranging from first principles equations to data-driven and AI-based approaches. Additionally, it is often necessary to repurpose and create models at different levels of fidelity to support various stages of the development process. For example, using a lower-fidelity model can help with performing trade-off studies, whereas a higher-fidelity model can be used for validating your embedded software before implementing it on the hardware. To aid the development of complex engineered systems, it’s important to use different approaches to create models at different levels of fidelity to support different stages of the development cycle. In this EXPO talk, learn about: 

Tools and techniques available for modeling dynamic systems, such as first principles modeling using Simulink^® and Simcape™, data-driven modeling using system identification and deep learning techniques, and grey-box modeling using parameter estimation techniques.

Integrating components of physical systems modeled using different techniques, all within a single simulation environment. 

Techniques for model manipulation and reuse, such as linearization and reduced-order modeling, for supporting use cases at various stages of the development cycle.