V
主页
京东 11.11 红包
A Constrained Control Perspective on Safe Learning-Based Control by M. Zeilinger
发布人
A Constrained Control Perspective on Safe Learning-Based Control by Melanie Zeilinger (ETH), Nov 18, 2020 Abstract: Various demonstrations show the potential of learning-based control paradigms. Providing safety guarantees when learning in closed-loop control systems, however, remains a central challenge for the widespread success of these promising techniques in real-life and industrial settings. Out of the different possible notions of safety, I will focus on the satisfaction of critical safety constraints in this talk, a common and intuitive form of specifying safety in many applications. We will then approach the question of safety for learning-based control from a constrained control perspective. Model predictive control (MPC) is an established control technique for addressing constraint satisfaction with demonstrated success in various industries. However, it requires a sufficiently descriptive system model, as well as a suitable formulation of the control objective to provide the desired guarantees and to be able to solve the problem via numerical optimization. In this talk, I will discuss how MPC can provide a flexible framework for safe learning-based control, allowing to overcome some of the individual difficulties of both MPC and available reinforcement learning methods. The first part will address learning for inferring a model of the system dynamics and how to use a characterization of the residual model uncertainty to design effective but cautious controllers. The main part of the talk will then focus on a recent approach leveraging MPC as a safety filter, which provides a modular scheme for augmenting high-performance learning-based controllers with constraint satisfaction properties. ... Bio: Melanie Zeilinger is an Assistant Professor at the Department of Mechanical and Process Engineering at ETH Zurich, Switzerland where she leads the Intelligent Control Systems group...
打开封面
下载高清视频
观看高清视频
视频下载器
Learning for Decision-Making under Uncertainty by Bartolomeo Stellato
Model Based Deep Learning: Applications to Imaging & Communications by Y. Eldar
Control Barrier Functions & Neural Networks for Handling Risk and Uncertainty...
Optimal and Distributed Control in Animals by Lisa (Jing Shuang) Li
Enabling Automatic Building Envelope Retrofits Using Controls & Machine Learning
Interactivity for Engineering Education: Automatic Control with Interactive Tool
Control Design Based on Deep Learning by Draguna Vrabie
Real-Time Optimization Algorithms for Nonlinear Model Predictive Control of ...
Machine Learning for Sparse Nonlinear Modeling & Control by S. Brunton @ACC2023
Bridging the Gap: Using Real World Problems to Unveil Deep Control Principles
Flight Testing Active Flutter Control Technology on a Conventional Configuration
Leveraging Learning & Opt.-based Planning for Multi-Robot Systems by P. Tokekar
The Online Convex Optimization Approach to Control by Elad Hazan
IFAC Industry Connect: Automation & Control: An Integral Part of Process...
Energy Maximizing Control of Wave Energy Systems – the COER Way! by J. Ringwood
Competence-aware Planning and Control by Hamidreza Modares
Towards Gain-optimal ISS Controllers for Finite State Systems by Antoine Girard
IFAC Industry Connect: Making the Leap from Academia to Entrepreneurship
Controlled Fun: Teaching Automatic Control with Gamification by Steffi Knorn
Power System Dynamics and Control——Structure, Data and Learning by David J. Hill
Towards Flow Control: From Boundary Layers to Wind Farms and Back Again by Gayme
The Role of Adaptation in Learning, Safety, and Optimality by Anuradha Annaswamy
Learning and Control for Safety, Efficiency, and Resiliency of Embodied AI
Why Would We Want a Multi-agent System Unstable? by Mrdjan Jankovic @ACC2023
Doing Robotics in Digital Labs: Or How Simulations Fuel Robotics Development
Resilience& Distributed Decision-making in a Renewable-rich Power Grid @IFAC2023
Model Predictive Control: A Rising Technology in the Automotive Industry
Entropy & Minimal Data Rates for State Estimation & Model Detection by Liberzon
Some Fundamental Limitations of Learning for Dynamics and Control by N Özay
Real-time Distributed Decision Making in Networked Systems by Na Li
Robust Online Convex Optimization for Disturbance Rejection by Peter Seiler
自动化控制考研阿祖们的必刷题库——控制研选150题
Is Resilience a Quality or a Quantity? by Erik Hollnagel @IFAC2023
Formal Methods for Safety-Critical Control by Calin Belta
Advanced, Adaptive and Flexible Algorithms for Decentralized Optimization
Learning Dynamics from Bilinear Observations by Sarah Dean
色情、语言与权力:资本逻辑的三大武器
Dual Control Revisited by Anders Rantzer @ CDC 2024
120 Years of Lyapunov's Methods by Stephen Boyd
有刷电机Simulink直连控制-二阶多智能体位置跟踪