Run-to-Run MPC Tuning via Gradient Descent

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The goal of this thesis is to propose pragmatic solutions to real challenges faced in the industry. The scope of this thesis encompasses two subjects: frequency-based structured controller synthesis for linear time-invariant (LTI) systems on one side, and ...

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 ...

The European Union's Green Deal aims for a 55% reduction in greenhouse gas emissions by 2030. To reach this goal, a massive integration of Renewable Energy Sources (RES) into the power grid is necessary. As RES become a large part of the electricity genera ...

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

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 ...

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

Drones hold promise to assist in civilian tasks. To realize this application, future drones must operate within large cities, covering large distances while navigating within cluttered urban landscapes. The increased efficiency of winged drones over rotary ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

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 ...

A novel approach for robust controller synthesis, which models uncertainty as an elliptical set, is proposed in the paper. Given a set of frequency response functions of linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems, the approac ...

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

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 ...

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method ...

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

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 ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

Daily manipulation tasks are characterized by regular features associated with the task structure, which can be described by multiple geometric primitives related to actions and object shapes. Only using Cartesian coordinate systems cannot fully represent ...

In this thesis, we address the complex issue of collision avoidance in the joint space of robots. Avoiding collisions with both the robot's own body parts and obstacles in the environment is a critical constraint in motion planning and is crucial for ensur ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

Run-to-Run MPC Tuning via Gradient Descent

Chattez avec Graph Search

Frequency domain data-driven robust and optimal control

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

Advanced control strategies to exploit the hydropower potential enhancing ancillary services provision to the power system

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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

Stability: a search for structure

Accurate and agile flight with winged drones

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

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

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform

Training Efficient Controllers via Analytic Policy Gradient

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Data-driven Methods for Control: from Linear to Lifting

Universal and adaptive methods for robust stochastic optimization

A geometric optimal control approach for imitation and generalization of manipulation skills

Implicit Distance Functions: Learning and Applications in Robotics

Robust Collaborative Learning with Linear Gradient Overhead

Distributionally Robust Linear Quadratic Control