Hi Johannes,
Yes, it is possible to estimate the unknown parameters of a non-linear dynamic model for a Remotely Operated Vehicle (ROV) using the available measurements like control inputs, thruster turns per second, and IMU (Inertial Measurement Unit) data. This process is known as parameter estimation or system identification.
In your case, since you already have a dynamic model and some known parameters, you can use a method like non-linear least squares or advanced techniques like the Extended Kalman Filter (EKF) or Particle Filters for parameter estimation. These methods can help you refine the unknown parameters of your model to better match the observed data.
Here's a broad overview of how this can be done:
1. Define the Model: You should have a mathematical representation of your ROV's dynamics. This model should include both known and unknown parameters.
2. Collect Data: Gather measurements from your ROV's operation. This should include inputs to the system (like control signals) and outputs (like position, velocity, which can be derived from IMU data).
3. Choose an Estimation Technique:
- Non-linear Least Squares: If the model is differentiable and the noise is Gaussian, this method can be effective.
- Extended Kalman Filter (EKF): Useful for systems with Gaussian noise and where the model can be linearized around the current estimate.
- Particle Filter: Suitable for systems with non-Gaussian noise or non-linear models that are difficult to linearize.
4. Estimation Process: Use the chosen technique to adjust the unknown parameters in your model so that the model's output closely matches your collected data.
5. Validation: After estimation, validate the model with a separate dataset to ensure the estimated parameters are accurate.
6. Refinement: Based on validation results, you might need to refine your model or estimation technique.
Hope this helps!
