Model predictive control python toolbox — do-mpc 4.6.4 documentation 模型預測控制Python工具箱

 https://www.do-mpc.com/en/latest/

do-mpc is a comprehensive open-source toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE). do-mpc enables the efficient formulation and solution of control and estimation problems for nonlinear systems, including tools to deal with uncertainty and time discretization. The modular structure of do-mpc contains simulation, estimation and control components that can be easily extended and combined to fit many different applications.

do-mpc 是一個用於穩健模型預測控制 (MPC) 和移動水平估計 (MHE) 的綜合開源工具箱。 do-mpc 能夠有效地制定和解決非線性系統的控制和估計問題，包括處理不確定性和時間離散化的工具。 do-mpc的模組化結構包含模擬、估計和控制組件，可輕鬆擴展和組合以適應許多不同的應用。

[2401.07369] CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design
https://arxiv.org/abs/2401.07369
https://www.arxiv-sanity-lite.com/?rank=pid&pid=2109.12147
CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design
https://arxiv.org/html/2401.07369v1
CoVO-MPC: CoVariance-Optimal MPC
https://github.com/LeCAR-Lab/CoVO-MPC

CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design - 智源社区论文
https://hub.baai.ac.cn/paper/8678fa80-06a6-4a14-ab76-dbd2f9960a02

Model predictive control  Covariance
Science and Technology / Environmental Sciences / Special Topics / Model

Predictive Control: Theory, Practices and Future Challenges Model Predictive Control in Hilbert Space
A. I. Propoi (Institute of Systems Analysis, Russian Academy of Sciences, Moscow, Russia)
https://link.springer.com/content/pdf/10.1023/B:AURC.0000038725.83948.ed.pdf
https://www.tandfonline.com/doi/pdf/10.1080/10556789408805574
https://www.researchgate.net/profile/Vladimir-Krivonozhko
https://dl.acm.org/doi/abs/10.1023/B%3AAURC.0000028321.05170.35

Mechanical Engineering Theory and Applications