Improving the control of prosthetic hands with tactile sensing

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This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

Increasing adoption of smart meters and phasor measurement units (PMUs) in power distribution networks are enabling the adoption of data-driven/model-less control schemes to mitigate grid issues such as over/under voltages and power-flow congestions. Howev ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

Trajectory planning through dynamical systems (DS) provides robust control for robots and has found numerous applications from locomotion to manipulation. However, to date, DS for controlling rhythmic patterns are distinct from DS used to control point to ...

Diauxie, or the sequential consumption of carbohydrates in bacteria such as Escherichia coli, has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive ad ...

The output of physical systems, such as the scrambled pattern formed by shining the spot of a laser pointer through fog, is often easily accessible by direct measurements. However, selection of the input of such a system to obtain a desired output is diffi ...

Humans manage to communicate action intentions in a non-verbal way, through body posture and movement. We start from this observation to investigate how a robot can decode a human's non-verbal cues during the manipulation of an object, with specific physic ...

Traffic management for large-scale urban road networks remains a challenging problem. Aggregated dynamical models of city-scale traffic, based on the macroscopic fundamental diagram (MFD), enable development of model-based control schemes for use with the ...

The increase of the effective efficiency of the internal combustion engines is important for the fuel consumption reduction and the CO2 emission reduction. The variable engine valve trains are important for the gas exchanges and influence directly the effe ...

In this paper, a new data-driven method for designing robust controllers is proposed for systemswith sector-bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex cons ...

A new data-driven approach using the frequency response function (FRF) of a system is proposed for designing robust-fixed structure digital controllers for particle accelerators' power converters. This design method ensures that the dynamics of a system ar ...

In this brief, the nanosatellite rendezvous and docking problem is tackled. It was never attempted for small spacecraft, as critical technologies, such as six-degree-of-freedom (DoF) micropropulsion systems, have only recently become available due to advan ...

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 ...

At present, most of the magnetic bearing system adopts the classical proportional-integral-derivative (PID) control strategy. However, the external disturbances, system parameter perturbations, and many other uncertain disturbances result in PID controller ...

Improving the control of prosthetic hands with tactile sensing

Chattez avec Graph Search

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Data-driven Methods for Control: from Linear to Lifting

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Robust optimization of control parameters for WEC arrays using stochastic methods

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

Memory of Motion for Initializing Optimization in Robotics

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

Learning dynamical systems with bifurcations

Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism

Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues

Model predictive perimeter control of MFD networks with guaranteed stability

Robust control for an electromagnetic actuator for a camless engine

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

A Robust Data-Driven Controller Design Methodology With Applications to Particle Accelerator Power Converters

H∞ and μ-Synthesis for Nanosatellites Rendezvous and Docking

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

Design of Magnetic Bearing Control System Based on Active Disturbance Rejection Theory