Lecture 29: State space representation

This video explains how a system can be modelled in state space form. Also shown is how to obtain transfer function from State space form See More

Posicast Control 5 - (In English)

In this video Posicast in closed-loop is illustrated using a gantry-crane system simulations.

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Lecture 19: Lead and PD compensator Design using Root Locus

This video discusses how a Proportional Derivative [PD] controller and a Lead compensator can be designed using Root Locus See More

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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SVD: Eigenfaces 2 [Matlab]

This video describes how the singular value decomposition (SVD) can be used to efficiently represent human faces, in the so-called "eigenfaces" (Matlab code, part 2).

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Lecture 14: Routh Hurwitz Criterion

Special cases in Routh Hurwitz criterion i.e (1) a row with only first entry being zero and (2) A row with all entries zero are considered. The video of a pr... See More

Laplace domain – tutorial 2: Region of Convergence (ROC)

In this video, we learn five golden rules on how to quickly find the Region of Convergence (ROC) of Laplace transform. Learn Signal Processing 101 in 31 lect...

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Laplace domain – tutorial 3: Laplace transform tables

In this video, we cover Laplace transform tables which help us to quickly find Laplace and inverse Laplace transforms. The main learning objective is to full...

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Control Bootcamp: Loop shaping

This video explores shaping the loop transfer function to have desirable sensitivity and complementary sensitivity.

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Relative Gain Array RGA Analysis

I cover how you can find the relative gain array from the steady state gain array, and interpret the results to determine which input to pair with which outp...

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Data-Driven Control: Balanced Truncation

In this lecture, we describe the balanced truncation procedure for model reduction, where a handful of the most controllable and observable state directions are kept for the reduced-order...

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Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a M...

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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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.

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State Space in Process Control

An overview on how we can derive a state space model from a given set of state variables and inputs, as well as an intro to deviation variables. This is part...

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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...

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RL Course by David Silver - Lecture 6: Value Function Approximation

A deep dive into incremental methods and batch methods of value function approximation.

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Control Bootcamp: Cruise Control Example with Proportional-Integral (PI) co...

In this video, we show that introducing integral control reduces the steady-state tracking error to zero in the cruise control example. We also use a more sophisticated model for the...

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State Space to Transfer Function

In this video we show how to transform a linear state space representation of a dynamic system to an equivalent transfer function representation. We will de...

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Peter Ponders PID, Natural Frequency vs Frequency of Acceleration

This video is intended for servo hydraulic system designers and those that program hydraulic servo systems but it does show how several of the techniques sho...

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Extremum Seeking Control in Simulink

This lecture explores extremum-seeking control (ESC) on a simple example in Matlab’s Simulink.

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Using a Homogeneous Transformation Matrix to Combine Rotation and Translatio...

In this video we discuss how to properly deal with coordinate frames that are both rotated and translated from one another. We develop a homogeneous transfo...

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Peter Ponders PID - Observers/Estimators

Observers can provide betters estimates of state variables when there is noise and low resolution feed back. This is often necessary to compute the higher d... See More

Fourier Series and Gibbs Phenomena [Python]

This video will describe how to compute the Fourier Series in Python and Gibbs Phenomena that appear for discontinuous functions.

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Manipulating Aerodynamic Coefficients

In this video we discuss some potential problems you may encounter when attempting to perform operations with dimensionless aerodynamic coefficients such as CL and CD.

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Root Locus Plot: Common Questions and Answers

In this video I go through some of the common questions I've received on my other root locus videos. 1) Why do we call the poles of a system the roots?2) How do I plot the damping ratio...

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