| Name | deepctools JSON |
| Version |
1.1.5
JSON |
| download |
| home_page | None |
| Summary | A wrapped package for Data-enabled predictive control (DeePC) implementation. Including DeePC and Robust DeePC design with multiple objective functions. |
| upload_time | 2024-12-09 10:18:41 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
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| keywords |
data-driven control
deepc
mpc
rdeepc
robust deepc
data-driven mpc
data-enabled predictive control
model-free-control
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
> Introduction to my developed deepctools toolbox for data-enabled predictive control.
>
> Github project: [link](https://github.com/QiYuan-Zhang/DeePCtools)
>
> Introduction: [link](https://qiyuan-zhang.github.io/my-toolbox/2024/04/15/Developed-deepctools.html)
---
# DeePCtools
A wrapped package for Data-enabled predictive control (DeePC) implementation. Including **DeePC** and **Robust DeePC** design with multiple objective functions.
If you have questions, remarks, technical issues etc. feel free to use the issues page of this repository. I am looking forward to your feedback and the discussion.
## I. How to use
This package operates within the Python framework.
### 1. Required packages
- Numpy
- Matplotlib
- CasADi     <-- 3 <= __version__ <= 4
### 2. Usage
- Download the [*deepctools*](https://github.com/QiYuan-Zhang/DeePCtools/tree/8dbc2458966214bf9885f4d622e20c3b840641e2/deepctools) file and save it to your project directory.
- Or install using pip
```
pip install deepctools
```
Then you can use the deepctools in your python project.
## II. deepctools toolbox organization
```
.
└── deeptools
├── hankel
├── deepctools
│ ├── initialize_DeePCsolver
│ ├── initialize_RDeePCsolver
│ └── solver_step
├── getCasadiFunc
└── DiscreteSimulator
```
Under this file and function, there is detailed explanation of the usage, inputs, outputs of the functions.
### 1. hankel
Construct Hankel matrix of order L based on data x
- The data x: $x \in \mathbb{R}^{T, n_x}$
- The Hankel matrix: $H_L(x) \in \mathbb{R}^{n_x L \times T - L + 1}$
### 2. deepctools
Formulate and solve the DeePC problem, including **DeePC** and **Robust DeePC** design.
Construct the nlp solver for DeePC using CasADi IPOPT sovler, only formulate the solver once at the first beginning.
In the online loop, no need to reformulate the NLP problem which saves lots of computational time.
Each iteration, only need provide updated parameters: $u_{ini}$, $y_{ini}$ (, $u_{ref}$, $y_{ref}$ if set-point changes during control).
> Objective function: $J = \Vert y - y^r \Vert_Q^2 + \Vert u_{loss} \Vert_R^2 + \mathcal{o}(\sigma_y, g)$
> $u_{loss}$ can be:
```
'u': u
'uus': u - u_ref
'du': delta u
```
There is a tutorial file in [`tutorial.py`](https://github.com/QiYuan-Zhang/DeePCtools/blob/8dbc2458966214bf9885f4d622e20c3b840641e2/tutorial.py).
a. initialize_DeePCsolver(uloss, opts)
Formulate the DeePC design with different loss on control inputs.
The optmization problem can be formulated as:
```
Standard DeePC design: | Equivalent expression
min J = || y - yref ||_Q^2 + || uloss ||_R^2 | min J = || Uf*g - yref ||_Q^2 + || uloss ||_R^2
s.t. [Up] [uini] | s.t. Up * g = uini
[Yp] * g = [yini] | Yp * g = yini
[Uf] [ u ] | ulb <= u <= uub
[Yf] [ y ] | ylb <= y <= yub
ulb <= u <= uub |
ylb <= y <= yub | uloss = (u) or (u - uref) or (du)
```
b. initialize_RDeePCsolver
Formulate the Robust DeePC design with slack variables and different loss on control inputs.
The optmization problem can be formulated as:
```
Robust DeePC design: | Equivalent expression
min J = || y - yref ||_Q^2 + || uloss ||_R^2 | min J = || Uf*g - ys ||_Q^2 + || uloss ||_R^2
+ lambda_y||sigma_y||_2^2 | + lambda_y||Yp*g-yini||_2^2
+ lambda_g||g||_2^2 | + lambda_g||g||_2^2
s.t. [Up] [uini] [ 0 ] | s.t. Up * g = uini
[Yp] * g = [yini] + [sigma_y] | ulb <= u <= uub
[Uf] [ u ] [ 0 ] | ylb <= y <= yub
[Yf] [ y ] [ 0 ] |
ulb <= u <= uub |
ylb <= y <= yub | uloss = (u) or (u - uref) or (du)
```
c. solver_step
Solve the optimization problem for one step, and output the optimized control inputs, operator g, and solving time.
### 3. getCasadiFunc
Construct the Function using CasADi
### 4. DiscreteSimulator
Construct the discrete system simulator for predicting next step
## III. Tutorial
This is a tutorial example to illustrate how to use the *deepctools* to develop and implement DeePC design to different processes.
### 1. Plant
A simple discrete-time nonlinear model of polynomial single-input-single-output system is used:
```
y(t) = 4 * y(t-1) * u(t-1) - 0.5 * y(t-1) + 2 * u(t-1) * u(t) + u(t)
```
The model has been crafted as a `Plant` class to facilitate its utilization.
Notice:
- This system is adopted from the [paper](https://ieeexplore.ieee.org/abstract/document/10319277).
- Note this plant is a nonlinear model which do not satisfy the assumption of Fundamental Lemma, the control performance can be bad.
- For your own project, you can replace this plant to your own system.
### 2. DeePC designs
Within the sample code, you have the option to specify either DeePC or Robust DeePC design, along with various objective functions. This segment is implemented within the `main` function.
### 3. Tutorial results
Feasible DeePC config:
```
DeePC | {Tini:1, Np:5, T:5, uloss:uus} | T merely influence the performance as long as T>=5
Robust DeePC | {Tini:1, Np:1, T:600, uloss:du} | T will influence the steady-state loss
Robust DeePC | {Tini:1, Np:1, T:600, uloss:uus} | T will influence the steady-state loss
Robust DeePC | {Tini:1, Np:1, T:600, uloss:u} | T will influence the steady-state loss
```
Figure of control peformance under first config:
## Application
This project has been applied and used in the following papers:
- **Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning**: [paper](https://arxiv.org/abs/2408.16338) & [source code](https://github.com/QiYuan-Zhang/Deep-DeePC)
```
@article{zhang2024deepdeepc,
title={Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learnin},
author={Zhang, Xuewen and Zhang, Kaixiang and Li, Zhaojian and Yin, Xunyuan},
journal={arXiv:2408.16338},
year={2024}
}
```
## License
The project is released under the APACHE license. See [LICENSE](https://github.com/QiYuan-Zhang/DeePCtools/blob/8dbc2458966214bf9885f4d622e20c3b840641e2/LICENSE) for details.
Copyright 2024 Xuewen Zhang
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Raw data
{
"_id": null,
"home_page": null,
"name": "deepctools",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "Xuewen Zhang <xuewen.zhang741@outlook.com>",
"keywords": "Data-driven control, DeePC, MPC, RDeePC, Robust DeePC, data-driven MPC, data-enabled predictive control, model-free-control",
"author": null,
"author_email": "Xuewen Zhang <xuewen.zhang741@outlook.com>",
"download_url": "https://files.pythonhosted.org/packages/a9/36/41b44276ab5cd133d0e65c7c7a7b867cb0f7e3d63496d6c22403c76cda3a/deepctools-1.1.5.tar.gz",
"platform": null,
"description": "\n> Introduction to my developed deepctools toolbox for data-enabled predictive control.\n> \n> Github project: [link](https://github.com/QiYuan-Zhang/DeePCtools) \n> \n> Introduction: [link](https://qiyuan-zhang.github.io/my-toolbox/2024/04/15/Developed-deepctools.html)\n\n---\n\n# DeePCtools\nA wrapped package for Data-enabled predictive control (DeePC) implementation. Including **DeePC** and **Robust DeePC** design with multiple objective functions.\n\nIf you have questions, remarks, technical issues etc. feel free to use the issues page of this repository. I am looking forward to your feedback and the discussion.\n## I. How to use\n\nThis package operates within the Python framework.\n\n### 1. Required packages\n\n- Numpy\n- Matplotlib\n- CasADi     <-- 3 <= __version__ <= 4\n\n### 2. Usage\n\n- Download the [*deepctools*](https://github.com/QiYuan-Zhang/DeePCtools/tree/8dbc2458966214bf9885f4d622e20c3b840641e2/deepctools) file and save it to your project directory.\n\n- Or install using pip\n\n```\n pip install deepctools\n```\nThen you can use the deepctools in your python project.\n\n## II. deepctools toolbox organization\n```\n. \n\u2514\u2500\u2500 deeptools \n \u251c\u2500\u2500 hankel \n \u251c\u2500\u2500 deepctools \n \u2502 \u251c\u2500\u2500 initialize_DeePCsolver\n \u2502 \u251c\u2500\u2500 initialize_RDeePCsolver\n \u2502 \u2514\u2500\u2500 solver_step\n \u251c\u2500\u2500 getCasadiFunc \n \u2514\u2500\u2500 DiscreteSimulator\n```\n\nUnder this file and function, there is detailed explanation of the usage, inputs, outputs of the functions. \n\n### 1. hankel\n\nConstruct Hankel matrix of order L based on data x \n\n- The data x: $x \\in \\mathbb{R}^{T, n_x}$\n- The Hankel matrix: $H_L(x) \\in \\mathbb{R}^{n_x L \\times T - L + 1}$\n\n### 2. deepctools\n\nFormulate and solve the DeePC problem, including **DeePC** and **Robust DeePC** design.\n\nConstruct the nlp solver for DeePC using CasADi IPOPT sovler, only formulate the solver once at the first beginning. \n\nIn the online loop, no need to reformulate the NLP problem which saves lots of computational time.\n\nEach iteration, only need provide updated parameters: $u_{ini}$, $y_{ini}$ (, $u_{ref}$, $y_{ref}$ if set-point changes during control).\n\n> Objective function: $J = \\Vert y - y^r \\Vert_Q^2 + \\Vert u_{loss} \\Vert_R^2 + \\mathcal{o}(\\sigma_y, g)$\n\n> $u_{loss}$ can be:\n\n``` \n 'u': u\n\n 'uus': u - u_ref\n\n 'du': delta u\n``` \n\nThere is a tutorial file in [`tutorial.py`](https://github.com/QiYuan-Zhang/DeePCtools/blob/8dbc2458966214bf9885f4d622e20c3b840641e2/tutorial.py).\n\na. initialize_DeePCsolver(uloss, opts) \n\nFormulate the DeePC design with different loss on control inputs.\n\nThe optmization problem can be formulated as:\n\n```\n Standard DeePC design: | Equivalent expression\n min J = || y - yref ||_Q^2 + || uloss ||_R^2 | min J = || Uf*g - yref ||_Q^2 + || uloss ||_R^2\n s.t. [Up] [uini] | s.t. Up * g = uini\n [Yp] * g = [yini] | Yp * g = yini\n [Uf] [ u ] | ulb <= u <= uub\n [Yf] [ y ] | ylb <= y <= yub\n ulb <= u <= uub |\n ylb <= y <= yub | uloss = (u) or (u - uref) or (du)\n```\n\n\nb. initialize_RDeePCsolver\n\nFormulate the Robust DeePC design with slack variables and different loss on control inputs.\n\nThe optmization problem can be formulated as:\n\n```\n Robust DeePC design: | Equivalent expression\n min J = || y - yref ||_Q^2 + || uloss ||_R^2 | min J = || Uf*g - ys ||_Q^2 + || uloss ||_R^2\n + lambda_y||sigma_y||_2^2 | + lambda_y||Yp*g-yini||_2^2\n + lambda_g||g||_2^2 | + lambda_g||g||_2^2\n s.t. [Up] [uini] [ 0 ] | s.t. Up * g = uini\n [Yp] * g = [yini] + [sigma_y] | ulb <= u <= uub\n [Uf] [ u ] [ 0 ] | ylb <= y <= yub\n [Yf] [ y ] [ 0 ] |\n ulb <= u <= uub |\n ylb <= y <= yub | uloss = (u) or (u - uref) or (du)\n```\n\nc. solver_step\n\nSolve the optimization problem for one step, and output the optimized control inputs, operator g, and solving time.\n\n\n### 3. getCasadiFunc\n\nConstruct the Function using CasADi\n\n### 4. DiscreteSimulator\n\nConstruct the discrete system simulator for predicting next step\n\n## III. Tutorial \n\nThis is a tutorial example to illustrate how to use the *deepctools* to develop and implement DeePC design to different processes.\n\n### 1. Plant\n\nA simple discrete-time nonlinear model of polynomial single-input-single-output system is used: \n\n```\n y(t) = 4 * y(t-1) * u(t-1) - 0.5 * y(t-1) + 2 * u(t-1) * u(t) + u(t)\n```\n\nThe model has been crafted as a `Plant` class to facilitate its utilization.\n\nNotice:\n\n- This system is adopted from the [paper](https://ieeexplore.ieee.org/abstract/document/10319277).\n- Note this plant is a nonlinear model which do not satisfy the assumption of Fundamental Lemma, the control performance can be bad.\n- For your own project, you can replace this plant to your own system.\n\n\n### 2. DeePC designs\n\nWithin the sample code, you have the option to specify either DeePC or Robust DeePC design, along with various objective functions. This segment is implemented within the `main` function.\n\n### 3. Tutorial results\n\nFeasible DeePC config: \n```\n DeePC | {Tini:1, Np:5, T:5, uloss:uus} | T merely influence the performance as long as T>=5 \n Robust DeePC | {Tini:1, Np:1, T:600, uloss:du} | T will influence the steady-state loss \n Robust DeePC | {Tini:1, Np:1, T:600, uloss:uus} | T will influence the steady-state loss\n Robust DeePC | {Tini:1, Np:1, T:600, uloss:u} | T will influence the steady-state loss\n```\n\nFigure of control peformance under first config:\n\n\n\n\n## Application\n\nThis project has been applied and used in the following papers:\n\n- **Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning**: [paper](https://arxiv.org/abs/2408.16338) & [source code](https://github.com/QiYuan-Zhang/Deep-DeePC)\n```\n @article{zhang2024deepdeepc,\n title={Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learnin},\n author={Zhang, Xuewen and Zhang, Kaixiang and Li, Zhaojian and Yin, Xunyuan},\n journal={arXiv:2408.16338},\n year={2024}\n }\n```\n\n\n## License\n\nThe project is released under the APACHE license. See [LICENSE](https://github.com/QiYuan-Zhang/DeePCtools/blob/8dbc2458966214bf9885f4d622e20c3b840641e2/LICENSE) for details.\n\nCopyright 2024 Xuewen Zhang\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. 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