Summer 2024
Planned algorithms to learn about:
- PID
- LQR
- Markov Chain Monte Carlo (MCMC)
To explor this type, I used the system of a 1-DOF Thrust Vector Control, i.e. the "rocket" is free to rotate in one axis, and the thrust is able to correct this only in that same axis.
- Controlled variable:
$\phi$ , representing the rotation in degrees of the rocket from upright - Actuator:
$\theta$ , representing the angle in degrees at which the thrust is applied.
The tune()
method allows for the user to tune
Thrust is created at the end of the rocket, thus creating a torque about the axis of rotation of the rocket. The magnitude of this torque varies with the angle of the thrust relative to that of the rocket (
We also know from angular kinematics that
From here, we use the control transfer function:
and our block chart (coming soon) to determine the closed loop transfer function
(where
Comparing this to the base characteristic equation
NOTE: The use of an integral gain
Notes on this can be found in /MCMC/MCMC_Notes.md . I experimented with this method through the example of an inverted pendulum system, and tuned it to be able to both swing up and down, from any starting position.