[](https://www.python.org/)
[](https://github.com/emalderson/ThePhish/tree/master/docker)
[](https://github.com/iftahnaf/pyvectorguidance)
[](https://github.com/iftahnaf/pyvectorguidance/blob/main/LICENSE)
# General Info
This repository implemented Vector Guidance methods for autonomous systems.
# Table of Contents
1. [About Vector Guidance](#about-vector-guidance)
2. [Install](#install)
3. [Usage](#usage)
4. [References](#references)
# About Vector Guidance
Vector Guidance are 3D optimal guidance methods for aerial systems.
The guidance laws based on a controller that minimized an finite LQ cost function with form of:
$$ J = \|\mathbf{y(t_f)}\| + k \int_{t_0}^{t_f} \|\mathbf{u(t)}\|^2 dt $$
Where:
- $y$ is the Zero-Effort-Miss variable
- $k$ is weight on the integration part of the cost
- $u$ is the controller
- $t_0$ is the initial time and $t_f$ is the final time.
Because the controller that minimized the LQ cost function is unbound, we define the maximum acceleration of the system as $u_m$, such that:
$\|\mathbf{u}\| \leq u_m$ while $t_0 \leq t \leq t_f$
**Note**: The value of $u_m$ is determine by the physical properties of the system (eg. thrusters saturations, aerodynamical constants)
# Install:
pip install pyvectorguidance
# Usage
```python
from VectorGuidance import VectorGuidance
r = np.random.rand(3) * np.random.uniform(40, 60, size=1)
v = np.random.rand(3) * np.random.uniform(5, 15, size=1)
rho_w = 9.81
rho_u = 15
gz = 9.81
tgo = VectorGuidance.interception_tgo_bounded(r, v, rho_u, rho_w)
u = VectorGuidance.interception_controller_bounded(r, v, rho_u, tgo, gz)
```
# References
1. S. Gutman and S. Rubinsky, "3D-nonlinear vector guidance and exo-atmospheric interception," in IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 4, pp. 3014-3022, Oct. 2015, doi: 10.1109/TAES.2015.140204.
2. Gutman, S. (2019). Exoatmospheric Interception via Linear Quadratic Optimization. Journal of Guidance, Control, and Dynamics.
3. S. Gutman, "Rendezvous and Soft Landing in Closed Form via LQ Optimization," 2019 27th Mediterranean Conference on Control and Automation (MED), Akko, Israel, 2019, pp. 536-540, doi: 10.1109/MED.2019.8798572.
Raw data
{
"_id": null,
"home_page": "https://github.com/iftahnaf/pyvectorguidance",
"name": "pyvectorguidance",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "Python,Vector Guidance",
"author": "Iftach Naftaly",
"author_email": "iftahnaf@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/0e/34/6a07bdc8dea4419cc12d3ba2400ae50ba2fdb369cd80defae0e4ff9f0194/pyvectorguidance-1.0.3.tar.gz",
"platform": null,
"description": "\n[](https://www.python.org/)\n[](https://github.com/emalderson/ThePhish/tree/master/docker)\n[](https://github.com/iftahnaf/pyvectorguidance)\n[](https://github.com/iftahnaf/pyvectorguidance/blob/main/LICENSE)\n\n\n# General Info\nThis repository implemented Vector Guidance methods for autonomous systems.\n\n# Table of Contents\n\n1. [About Vector Guidance](#about-vector-guidance)\n2. [Install](#install)\n3. [Usage](#usage)\n4. [References](#references)\n\n# About Vector Guidance\nVector Guidance are 3D optimal guidance methods for aerial systems.\n\nThe guidance laws based on a controller that minimized an finite LQ cost function with form of:\n\n$$ J = \\|\\mathbf{y(t_f)}\\| + k \\int_{t_0}^{t_f} \\|\\mathbf{u(t)}\\|^2 dt $$\n\nWhere:\n- $y$ is the Zero-Effort-Miss variable\n- $k$ is weight on the integration part of the cost\n- $u$ is the controller\n- $t_0$ is the initial time and $t_f$ is the final time.\n\nBecause the controller that minimized the LQ cost function is unbound, we define the maximum acceleration of the system as $u_m$, such that:\n\n$\\|\\mathbf{u}\\| \\leq u_m$ while $t_0 \\leq t \\leq t_f$\n\n**Note**: The value of $u_m$ is determine by the physical properties of the system (eg. thrusters saturations, aerodynamical constants)\n\n\n\n# Install:\n\n pip install pyvectorguidance\n\n# Usage\n\n```python\n\nfrom VectorGuidance import VectorGuidance\n\nr = np.random.rand(3) * np.random.uniform(40, 60, size=1)\nv = np.random.rand(3) * np.random.uniform(5, 15, size=1)\n\nrho_w = 9.81\nrho_u = 15\ngz = 9.81\n\ntgo = VectorGuidance.interception_tgo_bounded(r, v, rho_u, rho_w)\nu = VectorGuidance.interception_controller_bounded(r, v, rho_u, tgo, gz)\n\n\n```\n\n# References\n\n1. S. Gutman and S. Rubinsky, \"3D-nonlinear vector guidance and exo-atmospheric interception,\" in IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 4, pp. 3014-3022, Oct. 2015, doi: 10.1109/TAES.2015.140204.\n\n2. Gutman, S. (2019). Exoatmospheric Interception via Linear Quadratic Optimization. Journal of Guidance, Control, and Dynamics.\n\n3. S. Gutman, \"Rendezvous and Soft Landing in Closed Form via LQ Optimization,\" 2019 27th Mediterranean Conference on Control and Automation (MED), Akko, Israel, 2019, pp. 536-540, doi: 10.1109/MED.2019.8798572.\n",
"bugtrack_url": null,
"license": "Apache Software License",
"summary": "Vector Guidance methods implemented in Python.",
"version": "1.0.3",
"project_urls": {
"Download": "https://github.com/user/reponame/archive/v_01.tar.gz",
"Homepage": "https://github.com/iftahnaf/pyvectorguidance"
},
"split_keywords": [
"python",
"vector guidance"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "21038f1d0fcba16f59300c1684e1e4fd8bb97fab03265bbda2cb704213817578",
"md5": "4fc5fac31371f83904cdef215dd90c8e",
"sha256": "5f85a6f9e73ceaa5f0fbcee55455e870566c1352e8ba95a36d67afbbc748d516"
},
"downloads": -1,
"filename": "pyvectorguidance-1.0.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4fc5fac31371f83904cdef215dd90c8e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 8704,
"upload_time": "2023-09-25T12:38:27",
"upload_time_iso_8601": "2023-09-25T12:38:27.648888Z",
"url": "https://files.pythonhosted.org/packages/21/03/8f1d0fcba16f59300c1684e1e4fd8bb97fab03265bbda2cb704213817578/pyvectorguidance-1.0.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0e346a07bdc8dea4419cc12d3ba2400ae50ba2fdb369cd80defae0e4ff9f0194",
"md5": "c0d07dde0aaca91fa91b43781b144572",
"sha256": "53a4bae24d5a1eb09c5238895d198be83ffa07df181eb14b54cd912e4c7e7a66"
},
"downloads": -1,
"filename": "pyvectorguidance-1.0.3.tar.gz",
"has_sig": false,
"md5_digest": "c0d07dde0aaca91fa91b43781b144572",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 8397,
"upload_time": "2023-09-25T12:38:30",
"upload_time_iso_8601": "2023-09-25T12:38:30.816596Z",
"url": "https://files.pythonhosted.org/packages/0e/34/6a07bdc8dea4419cc12d3ba2400ae50ba2fdb369cd80defae0e4ff9f0194/pyvectorguidance-1.0.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-25 12:38:30",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "iftahnaf",
"github_project": "pyvectorguidance",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "numpy",
"specs": [
[
"==",
"1.26.0"
]
]
},
{
"name": "rich",
"specs": [
[
"==",
"13.3.1"
]
]
},
{
"name": "scipy",
"specs": [
[
"==",
"1.10.0"
]
]
}
],
"lcname": "pyvectorguidance"
}
JSON
