Automatic Code Generation Tool for Nonlinear Model Predictive Control with Jupyter

被引：3
|
作者：
Katayama, S. [1 ]
Ohtsuka, T. [1 ]
机构：
[1] Kyoto Univ, Grad Sch Informat, Dept Syst Sci, Sakyo Ku, Kyoto 6068501, Japan
来源：
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
关键词：
Predictive Control; Optimal Control; Nonlinear Control; Software Tools; ALGORITHM; OPTIMIZATION;
D O I：
10.1016/j.ifacol.2020.12.447
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present an automatic code generation tool, AutoGenU for Jupyter, for nonlinear model predictive control (NMPC) with a user-friendly and interactive interface utilizing JupyterLab and Jupyter Notebook. We utilize a symbolic computation package SymPy for automatic C++ code generation. We also developed numerical solvers of NMPC using the continuation/GMRES (C/GMRES) method and multiple-shooting-based C/GMRES method in C++. AutoGenU for Jupyter provides the simulation environment of NMPC with these solvers and visualization of the simulation results. We give an example of code generation and numerical simulation of a swing-up control of a cart pole using AutoGenU for Jupyter. Copyright (C) 2020 The Authors.
引用
收藏
页码：7033 / 7040
页数：8
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