
TinyEKF: Lightweight C/C++ Extended Kalman Filter with Python for prototypin...
TinyEKF is a simple C/C++ implementation of the Extended Kalman Filter that is general enough to use on different projects. In order to make it practical for running on Arduino, STM32, and...
See MoreZ-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...
See MoreAdvances 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...
See MoreWhy 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...
See MoreKoopman 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...
See MoreMATLAB Function: ztrans
ztrans(f) finds the Z-Transform of f. By default, the independent variable is n and the transformation variable is z. If f does not contain n, ztrans uses symvar.
See MoreMPCTools: Nonlinear Model Predictive Control Tools for CasADi (Python Interf...
This Python package is a collection of model predictive control tools that build on CasADi by providing a simpler interface. Along with the python package, there are a bunch of example files...
See MoreWhy 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...
See MoreModel predictive control python toolbox
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...
See MoreAdvances 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...
See MoreData-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...
See MoreSystem 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...
See MoreVibrational 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...
See MoreHow 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...
See MoreJupyter Notebook: Code used to generate vibrational control of inverted pend...
Jupyter Notebook: Code used to generate vibrational control of inverted pendulum figures
See MoreDynamic 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...
See MorePython Control Systems Library
The python-control package is a set of python classes and functions that implement common operations for the analysis and design of feedback control systems. The initial goal is to implement...
See MorePrincipal 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).
See MoreWind Tunnel Testing: Introduction and Data Acquisition
This is the first of our 3 part series on wind tunnel testing. In this video, we introduce the concept of wind tunnel testing as well as discuss the process for acquiring aerodynamic data in...
See MoreTwo Tank System: C MEX-File Modeling of Time-Continuous SISO System
This MATLAB example shows how to perform IDNLGREY modeling based on C MEX model files. It uses a simple system where nonlinear state space modeling really pays off.
See MoreWhy the Riccati Equation Is important for LQR Control
This Tech Talk looks at an optimal controller called linear quadratic regulator, or LQR, and shows why the Riccati equation plays such an important role in solving it efficiently. The talk...
See MoreSystem 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...
See MoreLearning Dynamic Systems & Control Engineering with a Video Game
Engineering Students at Northern Illinois University are learning one of their core subjects, Dynamic Systems & Control, with the aid of a video game.
See MoreHow Antennas Work
Antennas constitute as a major component in various communication systems, signal transmission and many others. It is important to understand how they work and create propagating waves in...
See MoreBridging the Gap: Using Real World Problems to Unveil Deep Control Principle...
This is a plenary lecture given at the 2020 IEEE Conference on Control Systems Technology, Montreal, Canada, August 24-26, 2020. There is no paper, but this is the video of the talk.
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