MPsee

Version 1.4 (3,11 Mo) par Sadegh Tajeddin
MPsee toolbox is an automatic tool for building Nonlinear Model Predictive Controllers
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Mise à jour 5 oct. 2017

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MPsee is a mathematical tool that collects all the fundamental information about any optimal control problem and then automatically generates fast online Nonlinear Model Predictive Controller (NMPC) for simulation and implementation purposes. It features a user interface in order to define an optimal control problem. Upon a proper problem definition, MPsee generates related MATLAB codes. Users can then utilize the provided Simulink library (NMPCLib.slx) in order to implement and simulate their NMPC in Simulink. MPsee generates NMPC controllers based on c/GMRES and Newton/GMRES real-time optimization methods and allows users to select between single-shooting and multiple-shooting approaches.
License:
The MPsee Toolbox is distributed under the Apache License, Version 2.0 (Apache-2.0), but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. It is the user's responsibility in assessing the correctness of the theory and software implementation before putting it to use in their own research or exploiting the results commercially.

Citation pour cette source

Sadegh Tajeddin (2024). MPsee (https://www.mathworks.com/matlabcentral/fileexchange/64608-mpsee), MATLAB Central File Exchange. Récupéré le .

Compatibilité avec les versions de MATLAB
Créé avec R2015b
Compatible avec toutes les versions
Plateformes compatibles
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Examples/Quadrotor/

Examples/Quadrotor/

Version Publié le Notes de version
1.4

- An issue is fixed inside the Coder function in order to allow building NMPC for systems with multiple inputs and also allow definition of systems without any inequality constraints
- An example of quadrotor flight control is included in the package
1.3.0.0

An issue with the Coder function is fixed that caused run-time error in calling f.m function.
1.2.0.0

An issue with initializing vector of exterior penalty weights for inequality constraint handling was fixed. Now, number of inequality constraints can be zero.
1.1.0.0