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A Two-Stage Approach for a Mixed-Integer Economic Dispatch Game in Integrated Electrical and Gas Distribution Systems

Ananduta, W. (author), Grammatico, S. (author)

We formulate for the first time the economic dispatch problem among prosumers in an integrated electrical and gas distribution system (IEGDS) as a game equilibrium problem. Specifically, by approximating the nonlinear gas-flow equations either with a mixed-integer second-order cone (MISOC) or a piecewise affine (PWA) model and by assuming...

journal article 2024

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A Bayes Risk-Based Cost Function to Allocate Scan Time for Object Detection

van Veldhuijzen, Emiel (author)

Advancements in radar technology such as phased array antennas, digital beamforming, and adaptable waveform generation have led to the flexibility in controlling radar resources such as scan time, beamwidth and bandwidth during a radar mission. This new flexibility has led to a new research topic, radar resource management. Radar resource...

master thesis 2023

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Loudspeaker Filter Design with AI: Communication, Evaluation and User Interaction

Nijenhuis, Jonathan (author), Zunderman, Timo (author)

This thesis describes the design and implementation of a controller and GUI for an AI loudspeaker filter design program. This program uses a genetic algorithm to create a combination of analog passive filters, one for each driver in a loudspeaker system. In the controller, two cost functions are designed to evaluate these filter combinations,...

bachelor thesis 2023

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Optimal Trajectory Planning for Mitigated Motion Sickness: Simulator Study Assessment

Jain, V. (author), Kumar, Sandeep Suresh (author), Papaioannou, G. (author), Happee, R. (author), Shyrokau, B. (author)

In the transition from partial to high automation, occupants will no longer be actively involved in driving. This will allow the use of travel time for work or leisure, where high comfort levels preventing motion sickness are required. In this paper, an optimal trajectory planning algorithm is presented in order to minimise motion sickness in...

journal article 2023

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Optimizing Task Waiting Times in Dynamic Vehicle Routing

Botros, Alexander (author), Gilhuly, Barry (author), Wilde, N. (author), Sadeghi, Armin (author), Alonso-Mora, J. (author), Smith, Stephen L. (author)

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art...

journal article 2023

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Groupwise registration for longitudinal MRI analysis of glioma based on deep learning

Chinea Hammecher, Claudia (author)

Glioma progression is monitored by routine MR scanning, enabling tumor growth evaluation with respect to earlier time-points. This growth may present both as a mass effect and as an extension of abnormalities into previously healthy tissue. To accurately quantify tumor growth and tumor-induced deformations, longitudinal intrasubject image...

master thesis 2022

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Uncertain uncertainty in data-driven stochastic optimization: towards structured ambiguity sets

Chaouach, L. (author), Boskos, D. (author), Oomen, T.A.E. (author)

Ambiguity sets of probability distributions are a prominent tool to hedge against distributional uncertainty in stochastic optimization. The aim of this paper is to build tight Wasserstein ambiguity sets for data-driven optimization problems. The method exploits independence between the distribution components to introduce structure in the...

conference paper 2022

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Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals

de Vries, J.M. (author), Trevisan, E. (author), van der Toorn, J. (author), Das, T. (author), Ferreira de Brito, B.F. (author), Alonso-Mora, J. (author)

In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive...

conference paper 2022

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Nash equilibrium seeking under partial-decision information: monotonicity, smoothness and proximal-point algorithms

Bianchi, M. (author), Grammatico, S. (author)

We consider Nash equilibrium problems in a partial-decision information scenario, where each agent can only exchange information with some neighbors, while its cost function possibly depends on the strategies of all agents. We characterize the relation between several monotonicity and smoothness assumptions postulated in the literature....

conference paper 2022

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Curve Tilting With Nonlinear Model Predictive Control for Enhancing Motion Comfort

Zheng, Y. (author), Shyrokau, B. (author), Keviczky, T. (author), Sakka, Monzer Al (author), Dhaens, Miguel (author)

The benefits of automated driving can only be fully realized if the occupants are protected from motion sickness. Active suspensions hold the potential to raise the comfort level in automated passenger vehicles by enabling new functionalities in chassis control. One example is to actively lean the vehicle body toward the center of the corner...

journal article 2022

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Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach

Kon, Johan (author), Bruijnen, Dennis (author), van de Wijdeven, Jeroen (author), Heertjes, Marcel (author), Oomen, T.A.E. (author)

Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in...

conference paper 2022

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Distributed Time-Varying Optimization of Second-Order Multiagent Systems Under Limited Interaction Ranges

Hong, Huifen (author), Baldi, S. (author), Yu, Wenwu (author), Yu, Xinghuo (author)

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the...

journal article 2022

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Local Stackelberg equilibrium seeking in generalized aggregative games

Fabiani, Filippo (author), Tajeddini, Mohammad Amin (author), Kebriaei, Hamed (author), Grammatico, S. (author)

We propose a two-layer, semi-decentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity...

journal article 2022

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Learning for Control: An Inverse Optimization Approach

Akhtar, Syed Adnan (author), Sharifi K., Arman (author), Mohajerin Esfahani, P. (author)

We present a learning method to learn the mapping from an input space to an action space, which is particularly suitable when the action is an optimal decision with respect to a certain unknown cost function. We use an inverse optimization approach to retrieve the cost function by introducing a new loss function and a new hypothesis class of...

journal article 2022

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Distributed Adaptive Optimization with Weight-Balancing

Yue, D. (author), Baldi, S. (author), Cao, Jinde (author), De Schutter, B.H.K. (author)

This article addresses the continuous-time distributed optimization of a strictly convex summation-separable cost function with possibly nonconvex local functions over strongly connected digraphs. Distributed optimization methods in the literature require convexity of local functions, or balanced weights, or vanishing step sizes, or algebraic...

journal article 2022

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A Two-Stage Bayesian optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation

Bertipaglia, A. (author), Shyrokau, B. (author), Alirezaei, Mohsen (author), Happee, R. (author)

This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the average sum of the states’...

conference paper 2022

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Optimal routing of individual vehicles in stochastic urban networks: A method to find optimal routes for generic cost functions, including reliability and travel time

Plevier, Louis (author)

Current day routing systems use predicted travel times to calculate optimal routes. Some routing algorithms even use predicted traffic lights states to optimize these routes and to improve the expected travel time. But all these methods optimize the expected travel time and do not take reliability into account. Reliability is a major service...

master thesis 2021

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A distributed forward-backward algorithm for stochastic generalized Nash equilibrium seeking

Franci, B. (author), Grammatico, S. (author)

We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized Nash equilibrium seeking algorithm based on the preconditioned forward-backward operator splitting for SGNEPs, where, at each iteration, the expected value of the...

journal article 2021

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Distributed Augmented Lagrangian Method for Link-Based Resource Sharing Problems of Multi-Agent Systems

Ananduta, W. (author), Nedic, Angelia (author), Ocampo-Martinez, Carlos (author)

A multi-agent optimization problem motivated by the management of energy systems is discussed. The associated cost function is separable and convex although not necessarily strongly convex and there exist edge-based coupling equality constraints. In this regard, we propose a distributed algorithm based on solving the dual of the augmented...

journal article 2021

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Multi-Population Aggregative Games: Equilibrium Seeking via Mean-Field Control and Consensus

Kebriaei, Hamed (author), Sadati-Savadkoohi, S. Jafar (author), Shokri, Mohammad (author), Grammatico, S. (author)

In this article, we extend the theory of deterministic mean-field/aggregative games to multipopulation games. We consider a set of populations, each managed by a population coordinator (PC), of selfish agents playing a global noncooperative game, whose cost functions are affected by an aggregate term across all agents from all populations. In...

journal article 2021