Casadi Interface for Optimum experimental design and Parameter Estimation and Identification Applications

casiopeia holds a user-friendly environment for optimum experimental design and parameter estimation and identification applications. It does so by providing Python classes that can be initialized with the problem specifications, while the computations can then easily be performed using the available class functions.

casiopeia uses the optimization framework CasADi to solve the resulting optimization problems.

Note

casiopeia is still in it’s testing state, and does not yet contain all the features it will provide in future versions. Therefore, you should check for updates on a regular basis.

In the following sections, you will receive the information necessary to obtain, install and use casiopeia. If you encounter any problems using this software, please feel free to submit your errors with a description of how they occurred to adrian.buerger@hs-karlsruhe.de.

New: try casiopeia live in your browser [1]

Contents

• 1. Get and install casiopeia
  • 1.1. Installation on Ubuntu 16.04
  • 1.2. Installation on Windows
  • 1.3. Recommendations
• 2. Defining a system
• 3. System simulation
• 4. Parameter estimation
  • 4.1. Parameter estimation from single experiments
  • 4.2. Parameter estimation from multiple experiments
• 5. Optimum experimental design
  • 5.1. Optimum experimental design of single experiments
  • 5.2. Optimum experimental design of multiple experiments
• 6. Sample applications
  • 6.1. Parameter estimation for a Lotka-Volterra predator-prey-model
  • 6.2. Parameter estimation for a pendulum model
  • 6.3. Parameter estimation for a model race car
  • 6.4. Optimum experimental design for a model race car

[1]	This service is at the moment limited to one user at a time, due to restricted resources. If your computations do no start immediately, there’s probably another user testing casiopeia at the moment.