This issue shall describe the approach to implement some simple (model-based) controllers for the simulated soft robotic arm. Obviously, first #25 needs to be completed.
To get the soft robot moving, it makes sense to implement some simple PID / PD controller in configuration-space. A PD controller for set-point regulation would look like this:
Then, we should consider some simple model-based controllers. In particular interesting for set-point regulation would be:
where the gains of the feedback component can be reduced or even set to zero.
This will require the identification of the elasticity matrix K and the gravitational term G(q). While we can set the gravity to zero for the start, we can identify the (diagonal) constant matrix K with the following strategy:
Apply a torque tau to the system.
Wait for the system to reach steady-state
Compute the K with K=tau/q
We can do that for a few points, and identify our optimal fit for K with least-squares
This issue shall describe the approach to implement some simple (model-based) controllers for the simulated soft robotic arm. Obviously, first #25 needs to be completed.
To get the soft robot moving, it makes sense to implement some simple PID / PD controller in configuration-space. A PD controller for set-point regulation would look like this:
Then, we should consider some simple model-based controllers. In particular interesting for set-point regulation would be:
where the gains of the feedback component can be reduced or even set to zero.
This will require the identification of the elasticity matrix
Kand the gravitational termG(q). While we can set the gravity to zero for the start, we can identify the (diagonal) constant matrixKwith the following strategy:tauto the system.KwithK=tau/qKwith least-squares