Publications related to Data-driven controller validation

Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform

A novel approach for linear parameter-varying (LPV) controller synthesis for adaptive rejection of time-varying sinusoidal disturbances is proposed. Only the frequency response data of a linear time-invariant (LTI) multiple-input multiple-output (MIMO) sys ...

2024

Data-driven LPV Disturbance Rejection Control with IQC-based Stability Guarantees for Rate-bounded Scheduling Parameter Variations

Alireza Karimi, Elias Sebastian Klauser

In this paper, the challenge of asymptotically rejecting sinusoidal disturbances with unknown time-varying frequency and bounded rate is explored. A novel data-driven approach for designing linear parameter-varying (LPV) con- troller is introduced, leverag ...

2024

Stabilization and Accuracy Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Alireza Karimi, Seyed Sohail Madani

Power-Hardware-In-the-Loop (PHIL) setups have gained high importance in validation of the performance of newly developed algorithms and devices with low risk and implementation cost. However, the interconnection of a power hardware with a simulated model v ...

2021

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

Amir Aminifar

Editor's notes: The cross-layer approach presented in this article involves co-designing a feedback controller's parameters together with the schedule of an Ethernet network used for communicating state information and control signals.-Samarjit Chakraborty ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Probabilistic Adaptive Control for Robust Behavior Imitation

Sylvain Calinon, Julius Maximilian Jankowski, Hakan Girgin

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling of demonstrated skills. To reproduce a learned skill with a real robot, a feedback controller ...

IEEE2021

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

Alireza Karimi, Seyed Sohail Madani

In this paper, a data-driven method for controller design with constraints on the positive-realness of closed-loop transfer functions over an arbitrary set of frequencies is proposed. The positive-realness of a closed-loop transfer function is represented ...

IEEE2020

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Maryam Kamgarpour, Luca Furieri, Na Li

It is known that the set of internally stabilizing controller

\mathcal{C}_{\text{stab}}

is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the {Youla parameterization}, and two recent notions are t ...

2020

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Alireza Karimi, Seyed Sohail Madani

Power-Hardware-in-the-Loop (PHIL) setups have gained high importance in validation of the performance of newly developed instruments and devices with low risk and implementation cost. However, the interconnection of a power hardware with a simulated model ...

2020

A data-driven approach to model-reference control with applications to particle accelerator power converters

Alireza Karimi, Achille Nicoletti, Michele Martino

A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can b ...

PERGAMON-ELSEVIER SCIENCE LTD2019

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Achille Nicoletti

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate ...

EPFL2018

Data-driven controller validation

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Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform

Data-driven LPV Disturbance Rejection Control with IQC-based Stability Guarantees for Rate-bounded Scheduling Parameter Variations

Stabilization and Accuracy Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

Probabilistic Adaptive Control for Robust Behavior Imitation

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

A data-driven approach to model-reference control with applications to particle accelerator power converters

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform

Stabilization and Accuracy Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Probabilistic Adaptive Control for Robust Behavior Imitation

Data-driven LPV Disturbance Rejection Control with IQC-based Stability Guarantees for Rate-bounded Scheduling Parameter Variations

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

A data-driven approach to model-reference control with applications to particle accelerator power converters