lmpc 0.5.0
A Python package for Linear Model Predictive Control, with generation of high-performant and embeddable code.
Requires Python: >=3.9

**lmpc** is a Python  package for Model Predictive Control (MPC) of linear systems, brining the functionality of the Julia package [LinearMPC.jl](https://github.com/darnstrom/LinearMPC.jl) to Python.
It aims to provide a user-friendly experience, while simultaneously being able to generate  _high-performant_ and _lightweight_ C-code that can easily be used on embedded systems. The package supports code generation for the Quadratic Programming solver [DAQP](https://github.com/darnstrom/daqp), and for explicit solutions computed by [ParametricDAQP.jl](https://github.com/darnstrom/ParametricDAQP.jl). 

A simplified version (see the, soon to be released, documentation for a more complete formulation) of the solved problem is

$$
\begin{align}
        &\underset{u_0,\dots,u_{N-1}}{\text{minimize}}&& \frac{1}{2}\sum_{k=0}^{N-1} {\left((Cx_{k}-r)^T Q (C x_{k}-r) + u_{k}^T R u_{k} + \Delta u_{k}^T R_r \Delta u_k\right)}\\
        &\text{subject to} &&{x_{k+1} = F x_k + G u_k}, \quad k=0,\dots, N-1\\
        &&& x_0 = \hat{x} \\
        &&& {\underline{b} \leq A_x x_k + A_u u_k  \leq \overline{b}}, \quad k=0, \dots, N-1
\end{align}
$$

where $\hat{x}$ is the current state and $r$ is the desired reference value of $Cx$.

## Installation
```bash
pip install lmpc
```

## Example 
The following code show a simple MPC example of controlling an inverted pendulum on a cart, inspired by [this](https://se.mathworks.com/help/mpc/ug/mpc-control-of-an-inverted-pendulum-on-a-cart.html) example in the Model Predictive Toolbox in MATLAB.

```python
import numpy
from lmpc import MPC,ExplicitMPC

# Continuous time system dx = A x + B u
A = numpy.array([[0, 1, 0, 0], [0, -10, 9.81, 0], [0, 0, 0, 1], [0, -20, 39.24, 0]])
B = 100*numpy.array([0,1.0,0,2.0])
C = numpy.array([[1.0, 0, 0, 0], [0, 0, 1.0, 0]])


# create an MPC control with sample time 0.01, prediction horizon 10 and control horizon 5 
Np,Nc = 10,5
Ts = 0.01
mpc = MPC(A,B,Ts,C=C,Nc=Nc,Np=Np);

# set the objective functions weights
mpc.set_objective(Q=[1.44,1],R=[0.0],Rr=[1.0])

# set actuator limits
mpc.set_bounds(umin=[-2.0],umax=[2.0])
```

A control, given the state `x` and reference value `r`, is computed with
```python
u = mpc.compute_control(x=[0,0,0,0],r=[1,0])
```

Embeddable C-code for the MPC controller is generated with the command
```python
mpc.codegen(dir="codgen_dir")
```
which produce _allocation-free_ C-code in the directory `codegen_dir` for setting up optimization problems and solving them with the Quadratic Programming solver [DAQP](https://github.com/darnstrom/daqp).

The C-function `mpc_compute_control(control, state, reference, disturbance)` computes the optimal control, given the current `state`, `reference`, and measured disturbances `disturbance`, which are all floating-point arrays. The optimal control is stored in the floating-point array `control`. 


## Citation
If you find the package useful, consider citing one of the following papers, which are the backbones of the package:
```
@article{arnstrom2022daqp,
  author={Arnström, Daniel and Bemporad, Alberto and Axehill, Daniel},
  journal={IEEE Transactions on Automatic Control},
  title={A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive {LDL}$^{T}$ Updates},
  year={2022},
  volume={67},
  number={8},
  pages={4362-4369},
  doi={10.1109/TAC.2022.3176430}
}
```

```
@inproceedings{arnstrom2024pdaqp,
  author={Arnström, Daniel and Axehill, Daniel},
  booktitle={2024 IEEE 63rd Conference on Decision and Control (CDC)}, 
  title={A High-Performant Multi-Parametric Quadratic Programming Solver}, 
  year={2024},
  volume={},
  number={},
  pages={303-308},
}
```