Publications related to Nintendo 64 controller

Data-driven fixed-structure frequency-based H2 and H∞ controller design

Alireza Karimi, Philippe Louis Schuchert, Vaibhav Gupta

The frequency response data of a generalized system is used to design fixed-structure controllers for the H2 and H∞ synthesis problem. The minimization of the two and infinity norm of the transfer function between the exogenous inputs and performance outpu ...

Elsevier BV2024

Tumescense monitoring system for diagnosing erectile dysfunction and methods of use

Nikolaos Stergiopoulos, Rodrigo Araujo Fraga Da Silva

Systems and methods for monitoring penile tumescence are provided that overcome the drawbacks of previously known systems by providing a wearable formed of a flexible and elastic tube having a plurality of sensors disposed on or embedded within it, the wea ...

2023

Robust Resource-Aware Self-Triggered Model Predictive Control

Colin Neil Jones, Yuning Jiang, Yingzhao Lian

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, in ...

2022

Frequency-domain data-driven position-dependent controller synthesis for Cartesian Robots

Alireza Karimi, Philippe Louis Schuchert

Cartesian robots have position-dependent dynamics that should be accounted for in high performance applications. Traditional methods design Linear Time Invariant (LTI) controllers which are robustly stable with respect to position variations, but require a ...

2023

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

David Atienza Alonso, Dario Floreano, Ricardo Andres Chavarriaga Lozano, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Ping-Keng Jao

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers’ performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learnin ...

2022

Control-oriented modeling and analysis of tubular dielectric elastomer actuators dedicated to cardiac assist devices

Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Armando Matthieu Walter, Ning Liu

This paper deals with the control-oriented modeling of a multilayered dielectric elastomer actuator based tube. The actuator is clamped at both sides and performs a radial displacement. The hyperelastic deformation and the viscoelastic performance, togethe ...

2022

Systematic extraction of a control-oriented model from perturbative experiments and SOLPS-ITER for emission front control in TCV

Artur Perek, Olivier Claude Martin Février, Mirko Wensing

Systematic extraction of locally valid dynamic models from experiments is necessary for controller design and the validation of high fidelity models. This paper describes the extraction of a dynamic model in the form of a transfer function, giving the dyna ...

IOP Publishing Ltd2022

Bio-inspired standing balance controller for a full-mobilization exoskeleton

Mohamed Bouri, Jemina Fasola, Tristan Hubert Vouga, Romain Pierre François Baud

The invention concerns a method of performing automatic standing balance of a user in a full mobilization exoskeleton with at least a foot and using at least two actuated degrees of freedom, wherein a controller uses information provided from sensors to pr ...

2021

Hierarchical Control of Islanded Microgrids with Flexible Structures

Pulkit Nahata

The primary objective of this doctoral thesis is the design and development of hierarchical control schemes for the overall operation and control of Islanded mGs (ImGs) with flexible structuresâ or, with no reference topologies. The control methodologies ...

EPFL2021

Review of control strategies for lower-limb exoskeletons to assist gait

Auke Ijspeert, Mohamed Bouri, Ali Reza Manzoori, Romain Pierre François Baud

Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Met ...

BioMed Central2021

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

Development of free-boundary equilibrium and transport solvers for simulation and real-time interpretation of tokamak experiments

Francesco Carpanese

To exploit fusion as a source of energy, a hot and dense confined plasma is needed. This is achieved in tokamaks by actively controlling the plasma state meaning shape, position and internal kinetic profiles. This thesis addresses two topics: the developme ...

EPFL2021

Data-Driven LPV Controller Design for Islanded Microgrids

Alireza Karimi, Seyed Sohail Madani

This paper presents a data-driven multivariable Linear Parameter Varying (LPV) controller design method. The synthesis process is based on frequency-domain data corresponding to different operating points of the plant. The performance and also stability co ...

ELSEVIER2021

Control Strategies for Lower-limb Exoskeletons: from Full Mobilization to Assistance of Balance

Romain Pierre François Baud

Bipedal locomotion is a remarkable feature of humans. This skill is necessary for the activities of daily living. Unfortunately, many people only partially benefit from it or miss it entirely because of a disability. This may result in a slower gait, less ...

EPFL2020

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

PolyMPC: An efficient and extensible tool for real‐time nonlinear model predictive tracking and path following for fast mechatronic systems

Colin Neil Jones, Petr Listov

This paper presents PolyMPC, an open‐source C++ library for pseudospectral‐based real‐time predictive control of nonlinear systems. It provides a necessary background on the computational aspects of the pseudospectral approximation of optimal control probl ...

2020

Data-Driven Distributed Combined Primary and Secondary Control in Microgrids

Alireza Karimi, Christoph Manuel Kammer, Seyed Sohail Madani

This paper presents a comprehensive data-driven distributed combined primary/secondary controller design method for microgrids. This method provides transient and steady-state performance including power-sharing and voltage and frequency restoration while ...

2021

Scalable Marine Bus-Tie Switch for Switchboard Interconnections

Drazen Dujic, Gabriele Ulissi

DC power distribution systems in marine applications are in the process of being established as the preferred solution for shipboard power distribution systems. This paper presents a bus-tie switch topology for the protection of such systems. The device is ...

2020

Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Nikolaos Geroliminis, Isik Ilber Sirmatel, Ye Ren

Most perimeter control methods in literature are the model-based schemes designing the controller based on the available accurate macroscopic fundamental diagram (MFD) function with well known techniques of modern control methods. However, accurate modelin ...

2020

On Gaussian Process Based Koopman Operators

Colin Neil Jones

Enabling analysis of non-linear systems in linear form, the Koopman operator has been shown to be a powerful tool for system identification and controller design. However, current data-driven methods cannot provide quantification of model uncertainty given ...

ELSEVIER2020