Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA（华盛顿大学机械工程系）；Data Science and Artificial Intelligence Department, The Aerospace Corporation, El Segundo, CA 90245（航空航天公司数据科学与人工智能部）；Department of Aeronautics, Imperial College, London SW7 2AZ, United Kingdom（帝国理工学院航空系）；Department of Applied Mathematics, University of Washington, Seattle, WA 98195（华盛顿大学应用数学系）；Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195（华盛顿大学电气与计算机工程系）
A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part II
Delft University of Technology（代尔夫特理工大学）；Institute for Problems of Mechanical Engineering of the Russian Academy of Sciences（俄罗斯科学院机械问题研究所）；ITMO University（ITMO大学）；Politecnico di Torino（都灵理工大学）
Technische Universität Berlin（柏林工业大学）；Zuse Institute Berlin（柏林祖斯研究所）；Univ. Grenoble Alpes, Inria, CNRS, LIG（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔实验室）
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA（加州理工学院工程与应用科学系）；Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA（加州理工学院喷气推进实验室）；Nonlinear Systems Laboratory, Massachusetts Institute of Technology, Cambridge MA, USA（麻省理工学院非线性系统实验室）
