Lecture

Optimal Distributed Control: Projected GD for Locally Optimal Controllers

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In course

ME-422: Multivariable control

This course covers methods for the analysis and control of systems with multiple inputs and outputs, which are ubiquitous in modern technology and industry. Special emphasis will be placed on discrete-time systems due to their relevance to digital and embe ...

Description

This lecture covers optimal distributed control, focusing on the challenges of implementing LQR controllers in large-scale systems due to measurement limitations. The instructor introduces the concept of using Gradient Descent (GD) to overcome these limitations and achieve locally optimal distributed controllers. The lecture delves into the theoretical foundations of GD, including the computation of the cost function and the convergence results. Additionally, the instructor discusses the application of GD in Stochastic LQR systems and the computation of the optimal policy. The lecture concludes with a detailed explanation of improving the controller through GD and the sparsity requirements for achieving optimal distributed control.

Instructor

Giancarlo Ferrari Trecate

Expertise Automatic control, Dependable control, Neural-network Control, Machine learning, Microgrids, Hybrid systems Mission See the webpage of the DECODE (Dependable Control and Decision) group. Teaching & PhD PhD Students Laura Meroi, Julien Pallage, Nicolas Léo Pierre Kirsch, Simone Baratto, Daniele Martinelli, Riccardo Cescon, Jakob Nylöf, Sabri El Amrani Past EPFL PhD Students Pulkit Nahata, Mustafa Sahin Turan, Baiwei Guo, Jean-Sébastien Brouillon, Clara Galimberti, Andrea Martin, Muhammad Zakwan,

Official source

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