do-mpc | Model predictive control python toolbox | Predictive Analytics library

by do-mpc Python Version: v4.5.1 License: LGPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | do-mpc Summary

do-mpc is a Python library typically used in Analytics, Predictive Analytics applications. do-mpc has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.

Model predictive control python toolbox

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do-mpc has a low active ecosystem.

It has 589 star(s) with 136 fork(s). There are 21 watchers for this library.

It had no major release in the last 12 months.

There are 57 open issues and 138 have been closed. On average issues are closed in 102 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of do-mpc is v4.5.1

Quality

do-mpc has 0 bugs and 0 code smells.

Security

do-mpc has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

do-mpc code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

do-mpc is licensed under the LGPL-3.0 License. This license is Weak Copyleft.

Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

Reuse

do-mpc releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

do-mpc saves you 1793 person hours of effort in developing the same functionality from scratch.

It has 5167 lines of code, 303 functions and 69 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed do-mpc and discovered the below as its top functions. This is intended to give you an instant insight into do-mpc implemented functionality, and help decide if they suit your requirements.

Prepare the NLP model .
Setup the discretization .
Calculate the prediction for a given index .
Set the default objective function .
Initialize the model .
Default plot function .
Make a single step .
Generate the MHE template .
Animation animation .
Set post processing .

Get all kandi verified functions for this library.

do-mpc Key Features

No Key Features are available at this moment for do-mpc.

do-mpc Examples and Code Snippets

No Code Snippets are available at this moment for do-mpc.

Community Discussions

Trending Discussions on do-mpc

Neural Network MPC

QUESTION

Neural Network MPC

Asked 2021-Jan-15 at 16:09

So I want to use Neural Network as my learned dynamic model function for MPC control in Python. I have not found any example/documention of doing this with open-source optimization packages like CASADI , GEKKO , do-mpc ? does any one have some reference/suggestion for achieving this? THANKS

Edit 01 a) I have tried CASADI + tensorflow model CASADI have a blog of how to use tensorflow model with CASADI. I am entirely not sure if I have done the implementation correctly as obviously I am not getting expected results. b) Upon looking on Internet there is "mpc. Pytorch" library which is a mpc toolbox which provides nn models as well. Not sure of its capability C) do-mpc which is based on CASADI is planning to integrate NN model. d) AS mentioned by @john gekko has the capability to use NN in mpc.

does any one know any other ways?

...

ANSWER

Answered 2021-Jan-14 at 21:18

Here is an example with a Neural Network and MPC: TCLab G - Nonlinear MPC. A potentially better way is to use an LSTM to emulate control (PID or MPC) as shown in a series of articles in Towards Data Science. This approach is also the basis for many explicit MPC publications that use methods for storage and retrieval of the solutions. I published an article on this method that includes a case study with ISAT and a Neural Network.

Hedengren, J. D. and Edgar, T. F., Approximate Nonlinear Model Predictive Control with In Situ Adaptive Tabulation, Computers and Chemical Engineering, Volume 32, pp. 706-714, 2008. Preprint

Using a storage and retrieval approach, you don't need to solve the MPC application each cycle, only use the machine learned prediction that is trained based on prior MPC moves.

Source https://stackoverflow.com/questions/65720961

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