Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from a linear–quadratic regulator (LQR). Also MPC has the ability to anticipate future events and can take control actions accordingly. PID controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry. (GPC) and (DMC) are classical examples of MPC. (Wikipedia).
(ML 10.7) Predictive distribution for linear regression (part 4)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
Weight Tuning for Model Predictive Controllers
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Use Tuning Adviser to adjust model predictive controller weights to improve controller performance. For more videos, visit http://www.mathworks.com/products/mpc/examples.ht
From playlist Control System Design and Analysis
(ML 10.4) Predictive distribution for linear regression (part 1)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
(ML 10.6) Predictive distribution for linear regression (part 3)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
(ML 10.5) Predictive distribution for linear regression (part 2)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
What is Model Predictive Control? | Understanding MPC, Part 2
Learn how model predictive control (MPC) works. - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M - Getting Started with Model Predictive Control Toolbox: http://bit.ly/2GskEY4 MPC uses a model of the plant to mak
From playlist Understanding Model Predictive Control
Model Predictive Control Design Parameters | Understanding MPC, Part 3
To successfully control a system using an MPC controller, you need to carefully select its design parameters. - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M - Design Controller Using MPC Designer: http://bit.ly/
From playlist Understanding Model Predictive Control
This lecture provides an overview of model predictive control (MPC), which is one of the most powerful and general control frameworks. MPC is used extensively in industrial control settings, and can be used with nonlinear systems and systems with constraints on the state or actuation inpu
From playlist Control Bootcamp
System Identification and Control Using OPC Data
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Improve process performance by designing and implementing a model predictive controller. Use OPC Toolbox and System Identification Toolbox to collect the input-output data
From playlist Control System Design and Analysis
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. Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. E. Kaiser, J. N. K
From playlist Data-Driven Control with Machine Learning
Comparison of ANN and LS prediction models for finite-control-set model predictive control of a PMSM
Video presentation PEMD 2020 - The 10th International Conference on Power Electronics, Machines and Drives Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor Contact: Departme
From playlist PEMD 2020 LEA Presentations
Nonlinear Model Predictive Control Design | Understanding MPC, Part 8
Learn how to design a nonlinear MPC controller for an automated driving application with Model Predictive Control Toolbox™ and Embotech FORCESPRO solvers. - Lane following using nonlinear model predictive control: https://bit.ly/3m3g19u The demonstration shows how to use the nonlinear M
From playlist Understanding Model Predictive Control
Marco Pavone: "On safe & efficient human-robot interactions via multimodal intent modeling & rea..."
Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop II: Safe Operation of Connected and Autonomous Vehicle Fleets "On safe and efficient human-robot interactions via multimodal intent modeling and reachability-based safety assurance" Marco Pavone - Stanford Uni
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
How to Design a Model Predictive Control Controller with Simulink | Understanding MPC, Part 6
Learn how to design an MPC controller for an autonomous vehicle steering system using Model Predictive Control Toolbox™. - Free Technical paper on Adaptive Cruise Controller with Model Predictive Control: http://bit.ly/2JhmOYr - Download model: http://bit.ly/2QcllZj - Learn more about Mod
From playlist Understanding Model Predictive Control
Why Use Model Predictive Control? | Understanding MPC, Part 1
Learn about the benefits of using model predictive control (MPC). - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M MPC uses the model of a system to predict its future behavior, and it solves an optimization prob
From playlist Understanding Model Predictive Control
Computational Principles of Sensorimotor Control (Lecture 1) by Daniel Wolpert
PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR,
From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)
Introduction to Reinforcement Learning (Lecture 01, Part 2/2, Summer 2023)
Initial lecture video on the course "Reinforcement Learning" at Paderborn University during the summer term 2023. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials 0:00 Basic terminology (state) 18:57 Basic terminology (action) 23:24 Basic
From playlist Reinforcement Learning Course: Lectures (Summer 2023)
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