29 citations · 160 across the 58 of their papers we have counts for
60 papers
On the suboptimality of stochastic MPC with varying constraint horizon
Allan Andre Do Nascimento, Andre Bertolace, Antonis Papachristodoulou +1
Enforcing stochastic state constraints over the full prediction horizon in Model Predictive Control (MPC) can be computationally demanding. Here we study stochastic MPC without ter…
A Prototyping Framework for Distributed Control of Multi-Robot Systems
Junaid Ahmed Memon, Allan Andre Do Nascimento, Kostas Margellos +1
This paper presents a prototyping framework for distributed control of multi-robot systems, aimed at bridging theory and practical testing of distributed optimization algorithms. U…
Model Predictive Control with Multiple Constraint Horizons
Allan Andre do Nascimento, Han Wang, Antonis Papachristodoulou +1
We propose a Model Predictive Control (MPC) formulation for nonlinear systems without terminal penalty or dedicated stabilizing terminal set, in which state constraints are enforce…
Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction
Boyu Pang, Kostas Margellos
Designing effective score functions in Conformal Prediction (CP) for time-series data remains challenging due to conservativeness and/or computational inefficiency. We propose Opti…
Data-Driven Performance Guarantees for Parametric Optimization Problems
Jingyi Huang, Paul Goulart, Kostas Margellos
We propose a data-driven method to establish probabilistic performance guarantees for parametric optimization problems solved via iterative algorithms. Our approach addresses two k…
Distributed equilibrium seeking in aggregative games: linear convergence under singular perturbations lens
Guido Carnevale, Filippo Fabiani, Filiberto Fele +2
We present a fully-distributed algorithm for Nash equilibrium seeking in aggregative games over networks. The proposed scheme endows each agent with a gradient-based scheme equippe…
Bridging conformal prediction and scenario optimization
Niall O'Sullivan, Licio Romao, Kostas Margellos
Conformal prediction and scenario optimization constitute two important classes of statistical learning frameworks to certify decisions made using data. They have found numerous ap…
Constraint Horizon in Model Predictive Control
Allan Andre Do Nascimento, Han Wang, Antonis Papachristodoulou +1
In this work, we propose a Model Predictive Control (MPC) formulation incorporating two distinct horizons: a prediction horizon and a constraint horizon. This approach enables a de…
Continuous-time Data-driven Barrier Certificate Synthesis
Luke Rickard, Alessandro Abate, Kostas Margellos
We consider the problem of verifying safety for continuous-time dynamical systems. Developing upon recent advancements in data-driven verification, we use only a finite number of s…
Predictive Control Barrier Functions: Bridging model predictive control and control barrier functions
Jingyi Huang, Han Wang, Kostas Margellos +1
In this paper, we establish a connection between model predictive control (MPC) techniques and Control Barrier Functions (CBFs). Recognizing the similarity between CBFs and Control…
Data-Driven Certificate Synthesis
Luke Rickard, Alessandro Abate, Kostas Margellos
We investigate the problem of verifying different properties of discrete time dynamical systems, namely, reachability, safety and reach-while-avoid. To achieve this, we adopt a dat…
Probabilistically safe controllers based on control barrier functions and scenario model predictive control
Allan Andre do Nascimento, Antonis Papachristodoulou, Kostas Margellos
Control barrier functions (CBFs) offer an efficient framework for designing real-time safe controllers. However, CBF-based controllers can be short-sighted, resulting in poor perfo…
Finite sample learning of moving targets
Nikolaus Vertovec, Kostas Margellos, Maria Prandini
We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case whe…
Safe and Stable Filter Design Using a Relaxed Compatibitlity Control Barrier -- Lyapunov Condition
Han Wang, Kostas Margellos, Antonis Papachristodoulou
In this paper, we propose a quadratic programming-based filter for safe and stable controller design, via a Control Barrier Function (CBF) and a Control Lyapunov Function (CLF). Ou…
Robust optimization for adversarial learning with finite sample complexity guarantees
André Bertolace, Konstatinos Gatsis, Kostas Margellos
Decision making and learning in the presence of uncertainty has attracted significant attention in view of the increasing need to achieve robust and reliable operations. In the cas…
Convex Co-Design of Control Barrier Function and Safe Feedback Controller Under Input Constraints
Han Wang, Kostas Margellos, Antonis Papachristodoulou +1
We study the problem of co-designing control barrier functions (CBF) and linear state feedback controllers for continuous-time linear systems. We achieve this by means of a single…
Probably approximately correct stability of allocations in uncertain coalitional games with private sampling
George Pantazis, Filiberto Fele, Filippo Fabiani +2
We study coalitional games with exogenous uncertainty in the coalition value, in which each agent is allowed to have private samples of the uncertainty. As a consequence, the agent…
Learning Robust Policies for Uncertain Parametric Markov Decision Processes
Luke Rickard, Alessandro Abate, Kostas Margellos
Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust m…
Homomorphically encrypted gradient descent algorithms for quadratic programming
André Bertolace, Konstantinos Gatsis, Kostas Margellos
In this paper, we evaluate the different fully homomorphic encryption schemes, propose an implementation, and numerically analyze the applicability of gradient descent algorithms t…
A priori data-driven robustness guarantees on strategic deviations from generalised Nash equilibria
George Pantazis, Filiberto Fele, Kostas Margellos
In this paper we focus on noncooperative games with uncertain constraints coupling the agents' decisions. We consider a setting where bounded deviations of agents' decisions from t…
Distributionally robust stability of payoff allocations in stochastic coalitional games
George Pantazis, Barbara Franci, Sergio Grammatico +1
We consider multi-agent coalitional games with uncertainty in the coalitional values. We provide a novel methodology to study the stability of the grand coalition in the case where…
Distributed Safe Control Design and Probabilistic Safety Verification for Multi-Agent Systems
Han Wang, Antonis Papachristodoulou, Kostas Margellos
We propose distributed iterative algorithms for safe control design and safety verification for networked multi-agent systems. These algorithms rely on distributing a control barri…
State Aggregation for Distributed Value Iteration in Dynamic Programming
Nikolaus Vertovec, Kostas Margellos
We propose a distributed algorithm to solve a dynamic programming problem with multiple agents, where each agent has only partial knowledge of the state transition probabilities an…
Multi-objective low-thrust spacecraft trajectory design using reachability analysis
Nikolaus Vertovec, Sina Ober-Blöbaum, Kostas Margellos
One of the fundamental problems in spacecraft trajectory design is finding the optimal transfer trajectory that minimizes the propellant consumption and transfer time simultaneousl…