Control theory | Computational mathematics | Dynamical systems
Data-driven control systems are a broad family of control systems, in which the identification of the process model and/or the design of the controller are based entirely on experimental data collected from the plant. In many control applications, trying to write a mathematical model of the plant is considered a hard task, requiring efforts and time to the process and control engineers. This problem is overcome by data-driven methods, which fit a system model to the experimental data collected, choosing it in a specific models class. The control engineer can then exploit this model to design a proper controller for the system. However, it is still difficult to find a simple yet reliable model for a physical system, that includes only those dynamics of the system that are of interest for the control specifications. The direct data-driven methods allow to tune a controller, belonging to a given class, without the need of an identified model of the system. In this way, one can also simply weight process dynamics of interest inside the control cost function, and exclude those dynamics that are out of interest. (Wikipedia).
Overview lecture for series on data-driven control. In this lecture, we discuss how machine learning optimization can be used to discover models and effective controllers directly from data. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectur
From playlist Data-Driven Control with Machine Learning
System Identification: Full-State Models with Control
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Data-Driven Control: Linear System Identification
Overview lecture on linear system identification and model reduction. This lecture discusses how we obtain reduced-order models from data that optimally capture input--output dynamics. https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Machine Learning Control: Overview
This lecture provides an overview of how to use machine learning optimization directly to design control laws, without the need for a model of the dynamics. Machine Learning Control T. Duriez, S. L. Brunton, and B. R. Noack https://www.springer.com/us/book/9783319406237 Closed-Loop Turb
From playlist Data-Driven Control with Machine Learning
Everything You Need to Know About Control Theory
Control theory is a mathematical framework that gives us the tools to develop autonomous systems. Walk through all the different aspects of control theory that you need to know. Some of the concepts that are covered include: - The difference between open-loop and closed-loop control - How
From playlist Control Systems in Practice
Data-Driven Control: Change of Variables in Control Systems
In this lecture, we discuss how linear control systems transform under a change of coordinates in the state variable. This will be useful to derive balancing transformations that identify the most jointly controllable and observable states. https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Data-Driven Control: The Goal of Balanced Model Reduction
In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and observable, to capture the most input—output energy. https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Data-Driven Dynamical Systems Overview
This video provides a high-level overview of this new series on data-driven dynamical systems. In particular, we explore the various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are 1)
From playlist Data-Driven Dynamical Systems with Machine Learning
Fuzzy control of inverted pendulum
Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance.
From playlist Demonstrations
Multicore Motor Control Using SoC Blockset
Are you facing challenges in developing multicore motor control systems, such as partitioning control algorithms, managing inter-processor communication, and synchronization? This video highlights issues that can arise when using a multicore MCU for motor control applications. You will
From playlist Embedded Systems | Developer Tech Showcase
System Identification: Regression Models
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Sparse Identification of Nonlinear Dynamics for Model Predictive Control
This lecture shows how to use sparse identification of nonlinear dynamics with control (SINDYc) with model predictive control to control nonlinear systems purely from data. Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. E. Kaiser, J. N. K
From playlist Data-Driven Control with Machine Learning
Formal verification and learning of complex systems - Professor Alessandro Abate
For slides, future Logic events and more, please visit: https://logic-data-science.github.io/?page=logic_learning Two known shortcomings of standard techniques in formal verification are the limited capability to provide system-level assertions, and the scalability to large-scale, complex
From playlist Logic and learning workshop
Machine Learning Control: Genetic Algorithms
This lecture provides an overview of genetic algorithms, which can be used to tune the parameters of a control law. Machine Learning Control T. Duriez, S. L. Brunton, and B. R. Noack https://www.springer.com/us/book/9783319406237 Closed-Loop Turbulence Control: Progress and Challenges
From playlist Data-Driven Control with Machine Learning
Data-driven nonlinear aeroelastic models of morphing wings for control
In this video, Urban Fasel describes a data-driven reduced-order aeroelastic modeling technique for morphing wings and shows its applicability for model predictive control. https://royalsocietypublishing.org/doi/full/10.1098/rspa.2020.0079 Data-driven nonlinear aero
From playlist Data-Driven Science and Engineering