add award

Author: xkhainguyen

CNAME renamed to docs/CNAME

@@ -3,64 +3,79 @@ title: Installation
 description: Install TinyMPC
 ---
 
-# How to install TinyMPC
+# Installing TinyMPC
 
-We offer user-friendly interfaces in high-level languages to enable low-level C++ code generation and verification, making them ready for deployment on embedded hardware. We also provide various [robotic control examples](examples.md), including the Crazyflie nano-quadrotor.
+A python interface is available that allows for [direct usage](./examples.md/#setup-problem) of TinyMPC. The interface can also be used to [generate C++ code](./examples.md/#code-generation) and an associated python module which allows for quick testing before integrating the generated code with your project. We provide examples for a few robots and have [firmware](https://github.com/RoboticExplorationLab/tinympc-crazyflie-firmware) for running TinyMPC on the Crazyflie 2.1 quadrotor.
 
-Check out our GitHub repositories for implementation details: [C++](https://github.com/TinyMPC/TinyMPC), [Python](https://github.com/TinyMPC/tinympc-python), [MATLAB](https://github.com/TinyMPC/tinympc-matlab), [Julia](https://github.com/TinyMPC/tinympc-julia)
+Source code is [here](https://github.com/TinyMPC/TinyMPC). Check out our other GitHub repositories for interface implementation details: [Python](https://github.com/TinyMPC/tinympc-python), [Julia](https://github.com/TinyMPC/tinympc-julia), [MATLAB](https://github.com/TinyMPC/tinympc-matlab).
 
 Visit our [GitHub Discussions](https://github.com/TinyMPC/discussions) page for any questions related to the solver!
 
 ---
 
-To get started simply choose your language interface and follow the easy installation instructions below (tested on Ubuntu):
+## Install from PyPI
 
-=== "C++"
+!!!note "Currently only available on Linux operating systems"
 
-    Clone the GitHub repository 
+``` bash
+pip install --upgrade tinympc
+```
 
-    `git clone https://github.com/TinyMPC/TinyMPC.git`
+Go to the [examples](./examples.md) page to see how to use TinyMPC.
 
-    Navigate to root directory and run
+<!-- To get started simply choose your language interface and follow the installation instructions (tested on Ubuntu 22.04): -->
 
-    `cd TinyMPC && mkdir build && cd build`
 
-    Run CMake configure step
+<!-- 
+=== "Python"
 
-    `cmake ..`
+    Make sure you have an up-to-date version of pip, then
 
-    Build TinyMPC
+    ```bash
+    pip install tinympc
+    ```
     
-    `cmake --build .`
+    Go to the [examples](./examples.md) page to see how to use TinyMPC.
 
-    Run an example
+=== "Julia"
 
-    `./examples/quadrotor_hovering`
+    !!! warning "The Julia interface is still under development. Do not expect correct behavior."
 
-=== "Python"
+    Clone the GitHub repository with submodules
 
-    Clone the GitHub repository with submodule
+    `git clone --recurse-submodules https://github.com/TinyMPC/tinympc-julia.git`
 
-    `git clone --recurse-submodules https://github.com/TinyMPC/tinympc-python.git`
+    Run the `interactive_cartpole_ext.ipynb` example
+    
+=== "MATLAB"
 
-    Install the package
+    !!! warning "The MATLAB interface is still under development. Do not expect correct behavior."
 
-    `cd tinympc-python & pip install -e .`
+    Clone the GitHub repository with submodules
 
-    Run the `interactive_cartpole.ipynb` example
+    `git clone --recurse-submodules https://github.com/TinyMPC/tinympc-matlab.git`
 
-=== "Julia"
+    Run the `interactive_cartpole.mlx` example -->
 
-    Clone the GitHub repository with submodule
+---
 
-    `git clone --recurse-submodules https://github.com/TinyMPC/tinympc-julia.git`
+## Build from source
 
-    Run the `interactive_cartpole_ext.ipynb` example
+If you'd like to build from source, you can do so by following these steps: 
 
-=== "MATLAB"
+Clone the repository and build the project
 
-    Clone the GitHub repository with submodule
+```bash
+git clone https://github.com/TinyMPC/TinyMPC.git
+cd TinyMPC
+mkdir build
+cd build
+cmake ..
+cmake --build .
+```
 
-    `git clone --recurse-submodules https://github.com/TinyMPC/tinympc-matlab.git`
+Run an example from the build directory
 
-    Run the `interactive_cartpole.mlx` example
+```bash
+./examples/quadrotor_hovering
+```

docs/get-started/examples.md

@@ -1,25 +1,29 @@
 ---
 title: Model
-description: Obtain the model
+description: How to obtain the dynamics model
 ---
 
 ## Linearization
 
-TinyMPC in its vanilla implementation can only handle linear dynamics, which means systems must be linearized about an equilibrium before being used by the solver. Extensions to TinyMPC allow the user to approximate a system's nonlinear dynamics by storing multiple linearizations, but we will start with only one.
+TinyMPC in its vanilla implementation can only handle linear dynamics, which means systems must be linearized about an equilibrium before being used by the solver. Extensions to TinyMPC allow the user to better approximate a system's nonlinear dynamics by storing multiple linearizations, but we will start here with only one.
 
-A discrete, linearized system is of the form $x_{k+1} = Ax_k + Bu_k$, where $x_k$ and $u_k$ are the state and control at the current time step, $A$ is the state-transition matrix, $B$ is the control or input matrix, and $x_{k+1}$ is the state at the next time step. For each of the examples given in [the previous page](./examples.md), the state-transition matrix $A$ and input matrix $B$ were computed from the system's continuous, nonlinear dynamics. (1)
-{.annotate}
+A discrete, linearized system takes the form $x_{k+1} = Ax_k + Bu_k$, where $x_k$ and $u_k$ are the state and control at the current time step, $A$ is the state-transition matrix, $B$ is the control or input matrix, and $x_{k+1}$ is the state at the next time step. Usually, this is derived from the continuous, nonlinear dynamics of the system, which takes the form $\dot{x} = f(x, u)$, where $f$ describes the instantaneous change in state at the current state and control input.
+
+This page describes how to derive the discrete, linearized system dynamics from the continuous, nonlinear system dynamics.
+
+<!-- For each of the examples given in [the previous page](./examples.md), the state-transition matrix $A$ and input matrix $B$ were computed from the system's continuous, nonlinear dynamics. (1)
+{.annotate} -->
 
-1. The system still needs to be discretized even if it is already linear. This can be done with the matrix exponential or by the same methods shown for the nonlinear system below.
+<!-- 1. The system still needs to be discretized even if it is already linear. This can be done with the matrix exponential or by the same methods shown for the nonlinear system below. -->
 
 ---
 
 ## Cart-pole example
 
-The continuous time dynamics for the cart-pole [have](https://courses.ece.ucsb.edu/ECE594/594D_W10Byl/hw/cartpole_eom.pdf){:target="_blank"} [been](https://www.matthewpeterkelly.com/tutorials/cartPole/index.html){:target="_blank"} [derived](https://underactuated.mit.edu/acrobot.html){:target="_blank"} [many](https://sharpneat.sourceforge.io/research/cart-pole/cart-pole-equations.html){:target="_blank"} [times](https://danielpiedrahita.wordpress.com/portfolio/cart-pole-control/){:target="_blank"}. For this example we'll use the convention from [this derivation](https://coneural.org/florian/papers/05_cart_pole.pdf){:target="_blank"}, where the pole is upright at $\theta=0$. If we ignore friction for this model, the only equations we care about in that derivation are (23) and (24). (1)
+The continuous time dynamics for the cart-pole [have](https://courses.ece.ucsb.edu/ECE594/594D_W10Byl/hw/cartpole_eom.pdf){:target="_blank"} [been](https://www.matthewpeterkelly.com/tutorials/cartPole/index.html){:target="_blank"} [derived](https://underactuated.mit.edu/acrobot.html){:target="_blank"} [many](https://sharpneat.sourceforge.io/research/cart-pole/cart-pole-equations.html){:target="_blank"} [times](https://danielpiedrahita.wordpress.com/portfolio/cart-pole-control/){:target="_blank"}. For this example we'll use the convention from [this derivation](https://coneural.org/florian/papers/05_cart_pole.pdf){:target="_blank"}, where the pole is upright at $\theta=0$. If we ignore friction, the only equations we care about in that derivation are (23) and (24). (1)
 {.annotate}
 
-1. If you're following along and want to use a cart-pole model that has friction, use equations (21) and (22).
+1. If you want to use a cart-pole model that has friction, use equations (21) and (22).
 
 Let's write those down in a dynamics function
 
@@ -131,4 +135,4 @@ Our integrator takes in the state and control at the current time step and integ
     ```
 
 
-Now all you have to do is save $A$ and $B$ and pass them to TinyMPC as shown in the problem setup section of [the examples page](examples.md).
+Now all you have to do is save $A$ and $B$ and pass them to TinyMPC as shown in the problem setup section of the [examples page](examples.md).

docs/get-started/installation.md

@@ -10,16 +10,22 @@ description: TinyMPC description and overview
   <img width="50%" src="media/darkmode-banner.png#only-dark" />
 </p>
 
+<p align="center" markdown>
+    [Get Started :material-arrow-right-box:](get-started/installation.md){.md-button}
+</p>
+
 TinyMPC is an open-source solver tailored for convex model-predictive control that delivers high speed computation with a small memory footprint. Implemented in C++ with minimal dependencies, TinyMPC is particularly suited for embedded control and robotics applications on resource-constrained platforms. TinyMPC can handle state and input bounds and second-order cone constraints. Python, Julia, and MATLAB interfaces are available to aid in generating code for embedded systems.
 
 !!! success "" 
 
     🏆 TinyMPC won the Best Paper Award in Automation and was a finalist for Best Conference Paper Award and Best Student Paper Award at [IEEE ICRA 2024](https://2024.ieee-icra.org/awards-and-finalists/)! Thank you to everyone who has used TinyMPC and provided feedback!
 
-[Get Started :material-arrow-right-box:](get-started/examples.md){.md-button}
-[ICRA Paper :simple-arxiv:](https://arxiv.org/abs/2310.16985){:target="_blank" .md-button}
-[CDC Paper :simple-arxiv:](https://arxiv.org/abs/2403.18149){:target="_blank" .md-button}
-[Watch the Video :fontawesome-brands-youtube:](https://www.youtube.com/watch?v=NKOrRyhcr6w){:target="_blank" .md-button}
+
+<p align="center" markdown>
+    [ICRA Paper :simple-arxiv:](https://arxiv.org/abs/2310.16985){:target="_blank" .md-button}
+    [Conic Code Gen :simple-arxiv:](https://arxiv.org/abs/2403.18149){:target="_blank" .md-button}
+    [Watch the Video :fontawesome-brands-youtube:](https://www.youtube.com/watch?v=NKOrRyhcr6w){:target="_blank" .md-button}
+</p>
 
 ---
 

docs/get-started/model.md

@@ -4,17 +4,10 @@ title: Inside TinyMPC
 
 # Inside TinyMPC
 
-!!! note ""
-
-    :fontawesome-solid-microchip: At its core, TinyMPC accelerates and compresses the ADMM algorithm by exploiting the structure of the MPC problem.
-
 Our 2024 ICRA submission video provides a concise overview of the solver:
 
 [Watch the Video :fontawesome-brands-youtube:](https://www.youtube.com/watch?v=NKOrRyhcr6w){:target="_blank" .md-button}
 
-Visit our [GitHub Discussions](https://github.com/TinyMPC/discussions) page for any questions related to the solver!
-
----
 
 ## Problem formulation
 

docs/index.md

@@ -12,15 +12,17 @@
 --md-accent-bg-color--light:         hsla(0, 0%, 100%, 0.7); */
 
 [data-md-color-scheme="tinympc-default"] {
-    --md-primary-fg-color:        #7b87eb;
-    --md-primary-fg-color--light: #f8f4fe;
-    --md-primary-fg-color--dark:  #1b1c23;
+    --md-primary-fg-color:        rgb(75, 105, 124);
+    /* --md-primary-fg-color--light: #f8f4fe;
+    --md-primary-fg-color--dark:  #1b1c23; */
 }
 
-[data-md-color-scheme="tinympc-slate"] {
+/* [data-md-color-scheme="tinympc-slate"] {
     --md-primary-fg-color:        #e97beb;
-    --md-primary-fg-color--light: #f4f6fe;
-    --md-primary-fg-color--dark:  #7a5632;
-    --md-primary-bg-color:        rgb(121, 22, 22);
-}
+} */
 
+:root {
+    --md-primary-fg-color:        #1a2c4d;
+    /* --md-primary-fg-color--light: #f8f4fe;
+    --md-primary-fg-color--dark:  #1b1c23; */
+}

docs/solver/solver.md

@@ -8,8 +8,8 @@ nav:
   - Get Started:
     - Installation: get-started/installation.md
     - Examples: get-started/examples.md
-    - Obtain the model: get-started/model.md
-  - The Solver: 
+    - Obtaining the model: get-started/model.md
+  - The Solver:
     - Inside TinyMPC: solver/solver.md
     - Background: solver/background.md
 
@@ -29,22 +29,25 @@ theme:
       toggle:
         icon: material/brightness-auto
         name: Switch to light mode
-      
+
     # Palette toggle for light mode
     - media: "(prefers-color-scheme: light)"
       scheme: default
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
-      primary: tinympc-default
+      # primary: custom
+      # primary: tinympc-default
 
     # Palette toggle for dark mode
     - media: "(prefers-color-scheme: dark)"
+      # scheme: tinympc-slate
       scheme: slate
       toggle:
         icon: material/weather-night
         name: Switch to system preference
-      primary: tinympc-slate
+      # primary: custom
+      # primary: tinympc-slate
 
   features:
     # - navigation.instant # Using navigation.instant disables table of contents auto-highlighting