Advances in feedforward control for measurable disturbances

The efficient compensation of load disturbances is one of the most important tasks in any control system. Most industrial processes are affected by disturbances and only feedback is commonly...

Z-Transform - Practical Applications

Covering practical applications of the Z-transform used in digital signal processing, for example, stability analysis and frequency response of discrete-time systems. Theory, C code, and...

System Identification: Koopman with Control

This lecture provides an overview of the use of modern Koopman spectral theory for nonlinear control. In particular, we develop control in a coordinate system defined by eigenfunctions of...

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...

Manuscript about ITISE: an Interactive Software Tool for System Identificati...

The paper describes the conceptual basis, main features and functionality of an interactive software tool developed in support of system identification education and discovery.

This...

A Tutorial on PES Pareto Methods for Analysis of Noise Propagation in Feedba...

This paper represents a tutorial on the so called PES Pareto methodology of analyzing the sources of noise in a feedback loop. Originally conceived for analyzing noise contributors in...

Koopman Spectral Analysis (Control)

In this video, we explore extensions of Koopman theory for control systems. Much of the excitement and promise of Koopman operator theory is centered around the ability to represent...

Advances in feedforward control for measurable disturbances (in Spanish)

The efficient compensation of load disturbances is one of the most important tasks in any control system. Most industrial processes are affected by disturbances and only feedback is commonly...

Vibrational Control in Insect Flight

Abstract: It is generally accepted among biology and engineering communities that insects are unstable at hover. However, existing approaches that rely on direct averaging do not fully...

Guaranteed Margins for LQR Regulators

John Doyle's famous paper! He presents a counterexample that shows that are no guaranteed margins for LQG systems. 

Why Digital Beamforming Is Useful for Radar

Learn how you can use digital beamformers to improve the performance and functions of radar systems. The MATLAB Tech Talk series on radar covered how to use radar to determine range, range...

Why Choose Model-Based Reinforcement Learning?

What is the difference between model-free and model-based reinforcement learning? Explore the differences and results as the learning models are applied to balancing a cart/pole system as an...

Perspectives on Control-Relevant Identification Through the Use of Interacti...

This paper presents a control-relevant identification methodology through an intuitive interactive tool called "Interactive Tool for Control Relevant Identification (ITCRI)". ITCRI...

Advanced process control (APC): Theory & Applications in SAGD

This webinar is presented by Thiago Avila and covers what APC is, why we do it, examples of APC in the SAGD industry, what optimization opportunities are available, and where this technology...

How I put the Google Maps Algorithm on my Autonomous Drone

This fully autonomous drone has an onboard computer ‘brain’, camera ‘eyes’, and an algorithm that generates the fastest path around unknown obstacles as they’re detected mid-flight. Let’s...

A Tutorial on PES Pareto Methods for Analysis of Noise Propagation in Feedba...

This is the recorded talk of the paper by the same title.

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

From the abstract

Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular...

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...

Nathan Kutz:"Data-driven Discovery of Governing Physical Laws"

Seminar by Dr.Nathan Kutz on "Data-driven Discovery of Governing Physical Laws" on 10/31/2018 CICS Seminar Series

Geodetic Coordinates: Computing Latitude and Longitude

In this video we show how to compute the geodetic latitude and terrestrial longitude if given the velocity north and east. This is useful for simulating a body moving over a spheroid Earth...

Multi-agent reinforcement learning: An overview

From the abstract:

Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity...

Dynamic Mode Decomposition (Overview)

In this video, we introduce the dynamic mode decomposition (DMD), a recent technique to extract spatio-temporal coherent structures directly from high-dimensional data. DMD has been widely...

Principal Component Analysis (PCA) 2 [Python]

This video describes how the singular value decomposition (SVD) can be used for principal component analysis (PCA) in Python (part 2).

Projectile Motion Practice Problems

In this video, practice along questions on an important topic of Kinematics i.e Projectile Motion. Practicing would help you remember the concepts and also understand them better.

System Identification: Dynamic Mode Decomposition with Control

This lecture provides an overview of dynamic mode decomposition with control (DMDc) for full-state system identification. DMDc is a least-squares regression technique based on the singular...