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Found 63 papers, 14 papers with code
Closed-Loop Neural Operator-Based Observer of Traffic Density
We consider the problem of traffic density estimation with sparse measurements from stationary roadside sensors.
Online Traffic Density Estimation using Physics-Informed Neural Networks
Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to model errors and noisy data.
Safe platooning control of connected and autonomous vehicles on curved multi-lane roads
This constructive barrier feedback acts as a dissipative term, slowing down the relative velocity toward obstacles without affecting the nominal controller's performance.
KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning
The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable.
Explainable Reinforcement Learning via Temporal Policy Decomposition
We present Temporal Policy Decomposition (TPD), a novel explainability approach that explains individual RL actions in terms of their Expected Future Outcome (EFO).
Risk-averse learning with delayed feedback
Furthermore, the one-point risk-averse learning algorithm attains sublinear regret under certain delay conditions, and the two-point risk-averse learning algorithm achieves sublinear regret with minimal restrictions on the delay.
Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret
At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player.
Robust Data-Driven Tube-Based Zonotopic Predictive Control with Closed-Loop Guarantees
This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise.
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck.
Real-time Hybrid System Identification with Online Deterministic Annealing
We introduce a real-time identification method for discrete-time state-dependent switching systems in both the input--output and state-space domains.
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
Object-centric (OC) representations, which represent the state of a visual scene by modeling it as a composition of objects, have the potential to be used in various downstream tasks to achieve systematic compositional generalization and facilitate reasoning.
Distributed Charging Coordination for Electric Trucks under Limited Facilities and Travel Uncertainties
The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties.
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Unlike non-Bayesian causal discovery, which relies on a single estimated causal graph and model parameters for assessment, evaluating BCD presents challenges due to the nature of its inferred quantity - the posterior distribution.
Differentially Private Dual Gradient Tracking for Distributed Resource Allocation
This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other agents.
Motion Planning for Identification of Linear Classifiers
A given region in 2-D Euclidean space is divided by a unknown linear classifier in to two sets each carrying a label.
Distributed Estimation by Two Agents with Different Feature Spaces
We consider the problem of estimation of a function by a system consisting of two agents and a fusion center.
Distributed Learning and Function Fusion in Reproducing Kernel Hilbert Space
We consider the problem of function estimation by a multi-agent system comprising of two agents and a fusion center.
Robust Data-Driven Kalman Filtering for Unknown Linear Systems using Maximum Likelihood Optimization
This paper investigates the state estimation problem for unknown linear systems subject to both process and measurement noise.
Distributed Charging Coordination of Electric Trucks with Limited Charging Resources
Electric trucks usually need to charge their batteries during long-range delivery missions, and the charging times are often nontrivial.
Hub-Based Platoon Formation: Optimal Release Policies and Approximate Solutions
We first show that the optimal release policy in the decentralized case, where the hubs do not exchange information, is to release all trucks at the hub when the number of trucks exceeds a threshold computed by dynamic programming.
Diffusion Based Causal Representation Learning
Causal reasoning can be considered a cornerstone of intelligent systems.
Social interactions for a sustainable lifestyle: The design of an experimental case study
Every day we face numerous lifestyle decisions, some dictated by habits and some more conscious, which may or may not promote sustainable living.
Large-Scale Multi-Fleet Platoon Coordination: A Dynamic Programming Approach
This paper presents a distributed framework for addressing multi-fleet platoon coordination in large transportation networks, where each truck has a fixed route and aims to maximize its own fleet's platooning profit by scheduling its waiting times at hubs.
What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab
Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency.
Distributed Nash Equilibrium Seeking with Stochastic Event-Triggered Mechanism
In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively.
Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers
The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems.
Concentration in Gossip Opinion Dynamics over Random Graphs
Leveraging matrix perturbation results, we show how such concentration can help study the effect of network structure on the expected final opinions in two cases: (i) When the influence of stubborn agents is large, the expected final opinions polarize and are close to stubborn agents' opinions.
Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels
To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle's index theory to design an index-based scheduling policy algorithm.
Resilient Set-based State Estimation for Linear Time-Invariant Systems Using Zonotopes
This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks.
Robust Data-Driven Predictive Control of Unknown Nonlinear Systems using Reachability Analysis
By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate reachable sets of an unknown nonlinear system.
Contract Composition for Dynamical Control Systems: Definition and Verification using Linear Programming
Contract theory has been proposed as a modular alternative to formal methods in control, in which specifications are defined by assumptions on the input to a component and guarantees on its output.
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics
The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases.
Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems
Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection.
Approximate Dynamic Programming for Platoon Coordination under Hours-of-Service Regulations
In our problem, trucks have fixed routes in a transportation network and can wait at hubs along their routes to form platoons with others while fulfilling the driving and rest time constraints.
Modeling and Analysis of a Coupled SIS Bi-Virus Model
We establish a sufficient condition and multiple necessary conditions for local exponential convergence to the boundary equilibrium (i. e., one virus persists, the other one dies out) of each virus.
Event-Triggered Distributed Model Predictive Control for Platoon Coordination at Hubs in a Transport System
This paper considers the problem of hub-based platoon coordination for a large-scale transport system, where trucks have individual utility functions to optimize.
Multi-Fleet Platoon Matching: A Game-Theoretic Approach
At a Nash equilibrium, vehicles with the same departure time are matched to form a platoon.
Verifying Contracts for Perturbed Control Systems using Linear Programming
We present a comparison-based framework, verifying that a perturbed system satisfies a contract by checking that the corresponding unperturbed system satisfies a robustified (and $\epsilon$-approximated) contract.
Variable-Speed Wind Turbine Control Designed for Coordinated Fast Frequency Reserves
It is shown that the NMP model is useful for designing a controller that coordinates FFR from wind with slow FCR from hydro turbines.
Dynamic Virtual Power Plant Design for Fast Frequency Reserves: Coordinating Hydro and Wind
To ensure frequency stability in future low-inertia power grids, fast ancillary services such as fast frequency reserves (FFR) have been proposed.
Identification of Linear Systems with Multiplicative Noise from Multiple Trajectory Data
An algorithm based on the least-squares method and multiple-trajectory data is proposed for joint estimation of the nominal system matrices and the covariance matrix of the multiplicative noise.
Suppressing the endemic equilibrium in SIS epidemics: A state dependent approach
This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure among subpopulations and focuses on the effect of social distancing to regulate the epidemic level.
Data-Driven Reachability Analysis from Noisy Data
We consider the problem of computing reachable sets directly from noisy data without a given system model.
Strategic Hub-Based Platoon Coordination under Uncertain Travel Times
We show that the platoon coordination game is a potential game when the travel times are either deterministic or stochastic, and the vehicles decide on their waiting times at the beginning of their journeys.
Verifying Compositional Refinement of Assume/Guarantee Contracts using Linear Programming
Verifying specifications for large-scale modern engineering systems can be a time-consuming task, as most formal verification methods are limited to systems of modest size.
Robust Data-Driven Predictive Control using Reachability Analysis
Instead of depending on a system model in traditional predictive control, a controller utilizing data-driven reachable regions is proposed.
Semantic Communications in Networked Systems: A Data Significance Perspective
We present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems.
Set-Membership Estimation in Shared Situational Awareness for Automated Vehicles in Occluded Scenarios
Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian.
Data-Driven Set-Based Estimation using Matrix Zonotopes with Set Containment Guarantees
We propose a method to perform set-based state estimation of an unknown dynamical linear system using a data-driven set propagation function.
Assume/Guarantee Contracts for Dynamical Systems: Theory and Computational Tools
Modern engineering systems include many components of different types and functions.
Causality Graph of Vehicular Traffic Flow
In an intelligent transportation system, the effects and relations of traffic flow at different points in a network are valuable features which can be exploited for control system design and traffic forecasting.
Data-Driven Reachability Analysis Using Matrix Zonotopes
In this paper, we propose a data-driven reachability analysis approach for unknown system dynamics.
Power Injection Attacks in Smart Distribution Grids with Photovoltaics
We study the worst-case attack strategy of a power injection attack against the physical layer of a smart distribution grid with a high penetration of photovoltaic resources.
A Scalable Nyquist Stability Criterion with Application to Power System Small-Signal Stability
A decentralized stability criterion is derived for a power system with heterogeneous subsystems.
Privacy Preserving Set-Based Estimation Using Partially Homomorphic Encryption
The set-based estimation has gained a lot of attention due to its ability to guarantee state enclosures for safety-critical systems.
Cryptography and Security Robotics
Numerical Investigation of Traffic State Reconstruction and Control Using Connected Automated Vehicles
The aim is to show the effect of the state reconstruction on the efficiency of the control, compared to the case using full information about the traffic.
Optimization and Control
Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies: Well-Posedness and Convergence
The operator is known to be well-posed for problems with finite states, but our analysis shows that it is also well-defined for the contractive models with infinite states studied.
Remote State Estimation with Smart Sensors over Markov Fading Channels
A smart sensor forwards its local state estimate to a remote estimator over a time-correlated $M$-state Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state.
Distributed Set-Based Observers Using Diffusion Strategies
Both algorithms utilize a set-based diffusion step, which decreases the estimation errors and the size of estimated sets, and can be seen as a lightweight approach to achieve partial consensus between the distributed estimated sets.
Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data
The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control.
Influence of Sensor Feedback Limitations on Power Oscillation Damping and Transient Stability
Using a two-machine power system model, it is shown that improved damping of inter-area oscillations must come at the cost of reduced transient stability margins, regardless of the control design method.
Distributed Design of Robust Kalman Filters over Corrupted Channels
We study distributed filtering for a class of uncertain systems over corrupted communication channels.
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective
In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach.
