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
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. Details can be found in https://nms.kcl.ac.uk/hk.lam/HKLam/index.php/demonstrations
From playlist Demonstrations
Melanie Zeilinger: "Learning-based Model Predictive Control - Towards Safe Learning in Control"
Intersections between Control, Learning and Optimization 2020 "Learning-based Model Predictive Control - Towards Safe Learning in Control" Melanie Zeilinger - ETH Zurich & University of Freiburg Abstract: The question of safety when integrating learning techniques in control systems has
From playlist Intersections between Control, Learning and Optimization 2020
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
Yafeng Yin: "Rhythmic Traffic Management and Control in Fully Automated Vehicle Environment"
Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop III: Large Scale Autonomy: Connectivity and Mobility Networks "Rhythmic Traffic Management and Control in Fully Automated Vehicle Environment" Yafeng Yin - University of Michigan Abstract: In this talk, we pr
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Lecture 14: Optimistic Concurrency Control
Lecture 14: Optimistic Concurrency Control MIT 6.824: Distributed Systems (Spring 2020) https://pdos.csail.mit.edu/6.824/
From playlist MIT 6.824 Distributed Systems (Spring 2020)
Lecture 12: Distributed Transactions
Lecture 12: Distributed Transactions MIT 6.824: Distributed Systems (Spring 2020) https://pdos.csail.mit.edu/6.824/
From playlist MIT 6.824 Distributed Systems (Spring 2020)
New Directions in Multiprocessor Synchronization
May 2, 2007 lecture by Maurice Herlihy for the Stanford University Computer Systems Colloquium (EE 380). Maurice talks about transactional memory, a computational model in which threads synchronize by optimistic, lock-free transactions -- this synchronization model promises to alleviate ma
From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)
Martin Odersky, "Working Hard to Keep It Simple" - OSCON Java 2011
Today's world of parallel and distributed computing poses hard new challenges for software development. A rapidly increasing number of developers now have to deal with races, deadlocks, non-determinism, and we are ill-equipped to do so. How can we keep things simple, in spite of the comple
From playlist OSCON 2011
Is Software Development The Most Difficult Job ITW
Software development is difficult, why is that? If you are wondering “is software development right for me”, “is software development a good career”, or if a software development career is an easy one it probably depends on how you think about problems. You don’t need to be a genius to be
From playlist Teamwork and Leadership
Constraint Enforcement for Improved Safety | Learning-Based Control, Part 2
Learn about the constraints of your system and how you can enforce those constraints so the system does not violate them. In safety-critical applications, constraint enforcement ensures that any control action taken does not result in the system exceeding a safety bound. Constraint enforce
From playlist Learning-Based Control
Kurt Kremer: Multiscale modeling for soft matter - Perspectives and challenges
Abstract: Material properties of soft matter are governed by a delicate interplay of energetic and entropic contributions. In other words, generic universal aspects are as relevant as local chemistry specific properties. Thus many different time and length scales are intimately coupled, wh
From playlist Numerical Analysis and Scientific Computing
What Is Robust Control? | Robust Control, Part 1
Watch the other videos in this series: Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 This videos covers a high-level introduction to robust control. The goal is to get
From playlist Robust Control
UHCL 06a Graduate Database Course - Undergraduate Review Part 2
This video corresponds to the unit 1 notes for a graduate database (dbms) course taught by Dr. Gary D. Boetticher at the University of Houston - Clear Lake (UHCL). It provides an undergraduate review of databases. Please note that this video is split into two parts.
From playlist UHCL Graduate Database Course
Rocky Mountain Ruby 2012 - Let's talk concurrency by Jose Valim
For a long time, the de facto way of doing multi-core concurrency was using threads. However, the complexity of manipulating threads and state affected negatively how developers perceive concurrency. Fortunately, languages like Clojure and Erlang implement new paradigms that aim to make co
From playlist Rocky Mountain Ruby 2012
We show the connection between the method of adjoints in optimal control to the implicit function theorem ansatz. We relate the costate or adjoint state variable to Lagrange multipliers.
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
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
(April 28, 2010) Rob Pike, a Principal Engineer at Google, Inc., discusses possible reasons why new computer languages keep appearing and why they led Google engineers to define yet another language, Go. Stanford University: http://www.stanford.edu/ Stanford School of Engineering: http:/
From playlist Engineering
LoneStarRuby 2015 Phoenix - Productive. Reliable. Fast. by Chris McCord
Phoenix is an Elixir web framework for building productive, reliable applications with the performance to take on the modern computing world. Together, we’ll review what makes Phoenix great and how it uses Elixir to optimize code for performance – without sacrificing programmer productivit
From playlist LoneStarRuby 2015
Eigenvalues and Modes of Linear Systems
In this video we discuss how the eigenvalues of the A matrix lead to the modes of a linear state space system. We will also examine how to chose initial conditions to excite a specific mode. In other words, we use a carefully chosen initial condition to ensure that the state response of
From playlist Control Theory