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Stochastic MPC for Controlling the Average Constraint Violation for Periodic Linear System with Additive Disturbance

Publications associées (28)

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023

Achieving Optimal Performance With Data-Driven Frequency-Based Control Synthesis Methods

Alireza Karimi, Philippe Louis Schuchert

Frequency Response Function (FRF)-based control synthesis methods for Linear Time-Invariant (LTI) systems have been widely used in control theory and industry. Recently, there has been renewed interest in these methods, employing numerical optimization too ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Dynamic pulse scheduling in ASDEX Upgrade: Disruption avoidance and investigation of the H-Mode density limit

Federico Alberto Alfredo Felici, Alessandro Pau, Bernhard Sieglin

Operation of a tokamak device requires the coordinated operation of a multitude of systems. The sequence of operations during a discharge is both too complex and fast for human interaction. A common way of operation is a predefined sequence of operations w ...

2023

Systematic design of a multi-input multi-output controller by model-based decoupling: a demonstration on TCV using multi-species gas injection

Basil Duval, Christian Gabriel Theiler, Cristian Galperti, Artur Perek

In this paper, we present the first results of a systematically designed multi-input multi-output gas-injection controller on Tokamak a Configuration Variable (TCV). We demonstrate the simultaneous real-time control of the NII emission front position and l ...

IOP Publishing Ltd2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Nicolas Würsch, Laure-Emmanuelle Perret Aebi, Marine Dominique Cauz

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023

DIET Controller: Dynamic Indoor Environment using Deep Reinforcement Learning

Arnab Chatterjee

Heating, Ventilation, and Air Conditioning (HVAC) Systems utilize much energy, accounting for 40% of total building energy use. The temperatures in buildings are commonly held within narrow limits, leading to higher energy use. Measurements from office bui ...

EPFL2023

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Colin Neil Jones, Yuning Jiang, Wenjie Xu, Bratislav Svetozarevic

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

IEEE2023

Data-enabled Predictive Control for Empty Vehicle Rebalancing

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

A critical operational challenge in Mobility-on-demand systems is the problem of imbalance between vehicle supply and passenger demand. However, conventional model-based methods require accurate parametric system models with complex nonlinear dynamics that ...

IEEE2023