Fixed order controller design tool
Project description
Focont
Static output feedback and fixed order controller design package for Python
Static output feedback (SOF)
The SOF is the simplest feedback controller structure. It basically feedbacks the system output to the system input after multiplying a constant gain matrix.
This package can calculate a stabilizing SOF gain which also optimizes the norm of the closed loop system.
However, this algorithm works under sufficient conditions. If the problem parameters (listed below) is not appropriate, the algorithm fails and prints an error message.
(See the article, https://journals.sagepub.com/doi/abs/10.1177/0142331220943071 , and the PhD thesis, http://hdl.handle.net/11693/54900 , for detailed information and analysis)
The algorithm is purposedly developed for discrete time systems, but it also works for continuous time systems when the SOF is calculated for the zero-order hold discretized version with a sufficiently large sampling frequency.
System definition can be provided by a JSON or MATLAB mat file. The required entries in the file are:
- A: (2d array) System matrix
- B: (2d array) Input matrix
- C: (2d array) Output matrix,
where (A, B)
is stabilizable and (C, A)
is observable (it is recommended
to solve for the observable part of the system if (C, A)
is detectable).
If you want to adjust const function weights, these should also be provided:
- Q: (2d array) Cost function weight for the state variables (similar to LQR problem)
- R: (2d array) Cost function weight for the system input.
If (A, B, C) defines a continuous time system, these should also be provided:
- type: (string) 'C' (which means continuous, 'D' means discrete)
- Ts: (number) Sampling period
Fixed order controller
Furthermore, the algorithm can be used to calculate fixed-order controllers. This need additional entries in the described JSON or mat file;
- structure: (string) 'FO' (meaning fixed-order or 'SOF' for SOF controller)
- controller_order: (integer) The order of controller (by default it is chosen as the number of inputs)
Installation
Create and activate a virtual environment (this step is not required but recommended). Then,
pip install -r requirements.txt
or
pip install numpy scipy
Finally,
pip install .
Example
from focont import foc, system
pdata = system.load(json_or_mat_filename)
foc.solve(pdata)
foc.print_results(pdata)
You can find json and mat file examples in the /tests
directory.
Project details
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