ParNMPC

KO | EN

gitlite — search

#python #ai #typescript #golang #go #android #linux #docker #nodejs #awesome #awesome-list #vue #ios #open-source

(★ 323)

A Parallel Optimization Toolkit for Nonlinear Model Predictive Control (NMPC)

파일 탐색기

최종 버전 다운로드 (.zip)

- - index.html
- - index.html
- - index.html
- - index.html
- - index.html
- - index.html
- - index.html
- - index.html
- - search_index.json
- - index.html
- - index.html
- 404.html
- damper.png
- dipc.png
- heli.png
- heli_comment.png
- heli_res_1.png
- heli_res_2.png
- index.html
- P_F_D.png
- P_F_D_exp.png
- quadrotor.png
- sitemap.xml
- sitemap.xml.gz
- vehicle.png
- NMPC_Problem_Formulation.m
- Simu_Matlab.m
- Simu_Matlab_Codegen.m
- Simu_Simulink.slx
- Simu_Simulink_Setup.m
- Timer.m
- - codeGen.m
  - DynamicSystem.m
  - enableM.m
  - setf.m
  - setInputName.m
  - setM.m
  - setParameterName.m
  - setStateName.m
  - showInfo.m
  - SIM_GEN_f_FuncGen.m
- - codeGen.m
  - NMPCSolver.m
  - setDescentRegularization.m
  - setHessianApproximation.m
  - setInvFxMethod.m
  - setNonsingularRegularization.m
  - showInfo.m
- - codeGen.asv
  - codeGen.m
  - createNonemptySolution_FuncGen.m
  - OCP_GEN_C_Cu_Cx_FuncGen.m
  - OCP_GEN_C_FuncGen.m
  - OCP_GEN_fdt_fudt_fxdt_FuncGen.m
  - OCP_GEN_fdt_FuncGen.m
  - OptimalControlProblem.m
  - setC.m
  - setDiscretizationMethod.m
  - setf.m
  - setG.m
  - setInputName.m
  - setL.m
  - setM.m
  - setParameterName.m
  - setStateName.m
  - setT.m
  - showInfo.m
- - C_Cu_Cx_Wrapper.m
  - C_Wrapper.m
  - f_fu_fx_Wrapper.m
  - f_Wrapper.m
  - fSim_Wrapper.m
- checkFeasibility.m
- checkOptions.m
- coarse_update_func.m
- createOptions.m
- createOutput.m
- createSolution.m
- fraction_to_boundary_parallel_func.m
- KKT_error_func.m
- NMPC_KKTError.m
- NMPC_LineSearch_Filter.m
- NMPC_LineSearch_Filter_PairEval.m
- NMPC_LineSearch_Merit.m
- NMPC_LineSearch_Merit_MeritEval.m
- NMPC_Solve.m
- NMPC_Solve_CodeGen.m
- NMPC_Solve_SearchDirection.m
- NMPC_SolveOffline.m
- OCP_Auu_Aux_Axx_Condensed.m
- OCP_C.m
- OCP_C_Cu_Cx.m
- OCP_F.m
- OCP_F_Fu_Fx.m
- OCP_G.m
- OCP_L.m
- OCP_L_Lu_Lx.m
- OCP_LB.m
- OCP_LB_LBu_LBx.m
- OCP_LBuu_LBux_LBxx_Estimate.m
- SIM_Plant_RK4.m
- NMPC_Problem_Formulation.m
- Simu_Matlab.m
- Simu_Matlab_Codegen.m
- Simu_Simulink.slx
- Simu_Simulink_Setup.m
- Timer.m
- - iiwa14.urdf
- - iiwa.cpp
  - iiwa14.h
  - tmwtypes.h
- CMakeLists.txt
- f_fu_fx_Wrapper.m
- f_Wrapper.m
- fSim_Wrapper.m
- NMPC_Problem_Formulation.m
- README.md
- Simu_Matlab.m
- Simu_Matlab_Codegen.m
- Timer.m
- NMPC_Problem_Formulation.m
- Simu_Matlab.m
- Simu_Matlab_Codegen.m
- Simu_Simulink.slx
- Simu_Simulink_Setup.m
- Timer.m
- circle.m
- NMPC_Problem_Formulation.m
- plot_res.m
- Simu_Matlab.m
- Simu_Matlab_Codegen.m
- Simu_Simulink.slx
- Simu_Simulink_Setup.m
- Timer.m
LICENSE
README.md

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ParNMPC Version 1903-1

### New Features in Version 1903-1:
* Primal-dual interior-point method
* Improved user interface
* Better performance
* Line search

## Introduction
Homepage: https://deng-haoyang.github.io/ParNMPC/

**`ParNMPC`** is a MATLAB real-time optimization toolkit for nonlinear model predictive control (NMPC). 
The purpose of **`ParNMPC`** is to provide an easy-to-use environment for NMPC problem formulation, closed-loop simulation, and deployment.

With **`ParNMPC`**, you can define your own NMPC problem in a very easy way and **`ParNMPC`** will automatically generate self-contained C/C++ code for single- or multi-core CPUs.

**`ParNMPC`** is very fast even with only one core (the computation time is usually in the range of $\mu$s), and a high speedup can be achieved when parallel computing is enabled.

### Highlights
* Symbolic problem representation
* Automatic parallel C/C++ code generation with OpenMP
* Fast rate of convergence (up to be superlinear)
* Highly parallelizable (capable of using at most N cores, N is the # of discretization steps)
* High speedup ratio
* MATLAB & Simulink

## Installation

1. Clone or download **`ParNMPC`**.
2. Extract the downloaded file.

## Requirements

* MATLAB 2016a or later
* MATLAB Coder
* MATLAB Optimization Toolbox
* MATLAB Parallel Computing Toolbox
* MATLAB Symbolic Math Toolbox
* Simulink Coder
* C/C++ compiler supporting parallel code generation

## Getting Started (MATLAB 2018b)

1. Select the Microsoft Visual C++ 2017 (C) compiler:
``` Matlab
>> mex -setup
```

2. Navigate to the *Quadrotor/* folder.
``` Matlab
>> cd  Quadrotor/
```

3. Open `NMPC_Problem_Formulation.m` and run.

4. Open `Simu_Simulink_Setup.m` and run.

5. Open `Simu_Simulink.slx` and run.

## Citing ParNMPC

Citing the parallel algorithm:

```
@article{deng2019parallel,
  title={A parallel Newton-type method for nonlinear model predictive control},
  author={Deng, Haoyang and Ohtsuka, Toshiyuki},
  journal={Automatica},
  volume={109},
  pages={108560},
  year={2019}}
```
Citing the toolbox (conference version):

```
@inproceedings{deng2018parallel,
  title={A parallel code generation toolkit for nonlinear model predictive control},
  author={Deng, Haoyang and Ohtsuka, Toshiyuki},
  booktitle={Proceedings of the 57th {IEEE} {C}onference on {D}ecision and {C}ontrol},
  pages={4920--4926},
  year={2018},
  address={Miami, USA}}
```

## License

ParNMPC is distributed under the BSD 2-Clause "Simplified" License.

ParNMPC

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ParNMPC

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README