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https://github.com/danielmartensson/matavecontrol

GNU Octave and MATLAB control toolbox
https://github.com/danielmartensson/matavecontrol

control-systems control-theory control-toolbox gnu-octave matlab

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GNU Octave and MATLAB control toolbox

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README 

          
# MataveControl V13.0

MataveControl is a basic toolbox for control engineering. The toolbox can be used for both GNU Octave and MATLAB®. Easy to use and easy to install. The main focus of MataveControl is to offer a control toolbox which can be used in both GNU Octave and MATLAB®. MataveControl has the same function names as MATLAB®'s Control System Toolbox, but the time discrete functions are included in the time continuous functions. Also the library is a very basic library so other developers can fast dive into the code.



# TODO



- `reg.m` This need to be fixed so it is the same as MATLAB

- `lqgreg.m` I don't know if `Matavecontrol` should keep it. It do the same as `reg.m`.

- More work on `gensig.m`

- Make `linprog.m` faster by using vectorization and not C-code style

- Make `arma.m` return a transfer function with discrete time `1`

- ARMA models should be simulated with Euler-method, instead of `filter`.



# Typical use



To use MataveControl, you should allways start with to create a transfer function or a state space model. Then you can use that mathematical model in almost all the function of MataveControl. 



Here is some examples when I use MataveControl. MATLAB pictures are from Umeå University.



Creating a transfer function in MATLAB®



![Transfer Function Example - MATLAB](examples/transferFunctionExmapleMATLAB.png)



Creating a transfer function in GNU Octave



![Transfer Function Example - Octave](examples/transferFunctionExmapleOctave.png)



Create a bode diagram plot in MATLAB®



![Bode Diagram Example - MATLAB](examples/bodeExampleMATLAB.png)



Create a bode diagram plot in GNU Octave



![Bode Diagram Example - Octave](examples/bodeExampleOctave.png)



Create a state space model in MATLAB®



![State Space Example - MATLAB](examples/stateSpaceExampleMATLAB.png)



Create a state space model in GNU Octave 



![State Space Example - Octave](examples/stateSpaceExampleOctave.png)



Do a step simulation in MATLAB®



![Step Simulation - MATLAB](examples/stepExampleMATLAB.png)



Do a step simulation in GNU Octave



![Step Simulation - Octave](examples/stepExampleOctave.png)



Convert a time continuous transfer function to a discrete transfer function in MATLAB®



![c2d Example - MATLAB](examples/c2dExampleMATLAB.png)



Convert a time continuous transfer function to a discrete transfer function in GNU Octave



![c2d Example - Octave](examples/c2dExampleOctave.png)



Do a nyquist diagram plot in MATLAB®



![Nyquist Example - MATLAB](examples/nyquistExampleMATLAB.png)



Do a nyquist diagram plot in GNU Octave



![Nyquist Example - Octave](examples/nyquistExampleOctave.png)



# Model Predictive Control - Linear programming & Quadratic programming with integral action



MataveControl have both linear programming MPC and quadratic programming MPC. There is equivalent C code at [CControl](https://github.com/DanielMartensson/CControl) if you want to apply them to embedded hardware. Select the programming method, quadratic or linear, that works with your situation. Both works fine, but quadratic programming (Hildreth's Method) is faster than linear programming (Simplex Method) in MATLAB. In C-code, it depends on your C-compiler. I have done more work on `qmpc.m` file, compared to `lmpc.m`, even if they both can do the same job.



If you want to understand practical and proper MPC without theory, you should:

* Acquire all the math from the thesis Model Predictive Control for an artifical pancreas - Matias Sørensen og Simon Kristiansen.pdf (Download it here at MataveControl)

* Stody the chapters 1 and 2 from Model Predictive Control System Design and Implementation Using MATLAB® by Liuping Wang (Important that must be chapter 1 and 2, ignore the rest of the chapters)

* Throw away your lecture notes/literature from your MPC class, they just wasting your time



https://github.com/DanielMartensson/MataveControl/blob/03df47fbb3688b9a541b172b13386fc612dd7bc7/examples/mpcExample.m#L1-L26



![MPC Example - MATLAB](examples/mpcExampleMATLAB.png)



# Model Predictive Control with Kalman-Bucy filter and plant simulation

This MPC is different from the above. This MPC contains a kalman filter and is meant to be used for implementation of MPC for micro controllers. This have a unique integral action, were the integral is added 

the reference vector `R` before optimizing with the QP-solver. The code follows the paper "Model Predictive Control for an artificial pancreas. Matias Sørensen og Simon Kristiansen", which can be found in this repository. 



Ther is also an equivalent C code example here: [CControl](https://github.com/DanielMartensson/CControl/blob/master/src/CControl/Documents/Examples/ControlEngineering/mpc.txt)



https://github.com/DanielMartensson/MataveControl/blob/8d15f7b0d237f5142def693d02c09a49e3c5283d/examples/kf_qmpcExample.m#L1-L114



This is the output where the plant model and the MPC model are not identical. Still, the error tracking can be minimized and the constraints are taken into account.



![MPC Example - MATLAB](examples/kf_qmpcExampleMATLAB.png)



# Install

To install MataveControl, download the folder "matave" and place it where you want it. Then the following code need to be written inside of the terminal of your MATLAB® or GNU Octave program. 



```matlab

path('path/to/the/folder/matave', path)

savepath

```

Example of a typical path.



```matlab

path('C:\Users\dmn\Documents\Octave\matave\', path)

savepath

```



# Update

Write this inside the terminal. Then MataveControl is going to download new .m files to MataveControl from GitHub



```matlab

mc.updatematavecontrol

```