Lecture 16: More on Root Locus and Gain Compensation

Extra numerical problems using Root locus is discussed in this video. Then follows, Gain compensation using Root Locus See More

Fuzzy Logic, Part 3: Design and Applications of a Fuzzy Logic Controller

This video walks you through the process of designing a fuzzy inference system that can balance a pole on a cart. You can design a fuzzy logic controller using just experience and intuition...

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Routh-Hurwitz Criterion, Beyond Stability

This video explains of few uses of the Routh-Hurwitz Criterion that go beyond simply determining how many poles exist in the right half plane. I cover how to determine gain margin and how...

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The Routh-Hurwitz Stability Criterion

In this video we explore the Routh Hurwitz Stability Criterion and investigate how it can be applied to control systems engineering. The Routh Hurwitz Stabi...

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Posicast Control 4 - ( In English )

This video continues to explore the gantry crame control simulations in open-loop- The main focus is the half-cycle Posicast.

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Lecture 20: PID and Lag-Lead Compensator Design using Root Locus

Design of a Proportional-Integral-Derivative [PID] controller and a lag-lead controller using Root Locus has been discussed in this video with numerical exam... See More

A Nonlinear, 6 DOF Dynamic Model of an Aircraft: the Research Civil Aircraft...

In this video we develop a dynamic model of an aircraft by describing forces and moments generated by aerodynamic, propulsion, and gravity that act on the aircraft. This video outlines the...

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Identifying Dominant Balance Physics from Data - Jared Callaham

This video illustrates a new algorithm to identify local dominant physical balance relations from multiscale spatiotemporal data.

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Relationship Between Poles and Performance of a Dynamic System

In this video we establish the relationship between pole locations and associated performance of a dynamic system. This relationship is useful to translate ...

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Stability and Eigenvalues [Control Bootcamp]

Here we discuss the stability of a linear system (in continuous-time or discrete-time) in terms of eigenvalues. Later, we will actively modify these eigenvalues, and hence the dynamics...

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Laplace domain – tutorial 5: Inverse Laplace transform

In this video, we cover inverse Laplace transform which enables us to travel back from Laplace to the time domain. We will learn how to use simple tricks alo...

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Cayley-Hamilton Theorem [Control Bootcamp]

Here we describe the Cayley-Hamilton Theorem, which states that every square matrix satisfies its own characteristic equation. This is very useful to prove results related to...

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Degrees of Controllability and Gramians [Control Bootcamp]

This lecture discusses degrees of controllability using the controllability Gramian and the singular value decomposition of the controllability matrix.

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Frequency domain – tutorial 3: filtering (periodic signals)

In this video, we learn about filtering which enables us to manipulate the frequency content of a signal. A common filtering application is to preserve desi...

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Control Bootcamp: Linear Quadratic Gaussian (LQG)

This lecture combines the optimal full-state feedback (e.g., LQR) with the optimal full-state estimator (e.g., LQE or Kalman Filter) to obtain the sensor-based linear quadratic Gaussian (LQG...

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Robust Principal Component Analysis (RPCA)

Robust statistics is essential for handling data with corruption or missing entries. This robust variant of principal component analysis (PCA) is now a workhorse algorithm in several fields...

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Data-Driven Control: Eigensystem Realization Algorithm Procedure

In this lecture, we describe the eigensystem realization algorithm (ERA) in detail, including step-by-step algorithmic instructions.

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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and ...

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

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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.

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Cascade Control Intro

How can we improve the disturbance rejection of our controllers using additional, relevant measurements? Tune in to find out!

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Peter Ponders PID-Fuzzy Logic vs PID

There are many academic and engineering papers showing how good fuzzy logic control is relative to PID control. Every FL vs PID paper I have seen compares...

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RL Course by David Silver - Lecture 8: Integrating Learning and Planning

Introduces model-based RL, along with integrated architectures and simulation based search.

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Understanding Model Predictive Control, Part 3: MPC Design Parameters

To successfully control a system using an MPC controller, you need to carefully select its design parameters. This video provides recommendations for choosing the controller sample time...

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Dynamic Modeling in Process Control

I'll show you how we can build the dynamic models necessary to derive process transfer functions as an introduction to process control.

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Peter Ponders PID - T0P1 Part 4, Misc Topics

This video covers another way to compute symbolic gains, the difference between having the P gain act on the error or just the feedback, extending bandwidt...

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