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Found 61 papers, 7 papers with code
Safe Reinforcement Learning Using Black-Box Reachability Analysis
Reinforcement learning (RL) is capable of sophisticated motion planning and control for robots in uncertain environments.
Identifying Causal Structure in Dynamical Systems
In this paper, we propose a method that identifies the causal structure of control systems.
Logical Zonotopes: A Set Representation for the Formal Verification of Boolean Functions
A logical zonotope, which is a new set representation for binary vectors, is introduced in this paper.
Formal Verification with Constrained Polynomial Logical Zonotope
To address this, we formulate constrained polynomial logical zonotopes, which maintain the computational efficiency and exactness of polynomial logical zonotopes for reachability analysis while supporting exact intersections.
Adaptive Sampling of Algal Blooms Using Autonomous Underwater Vehicle and Satellite Imagery: Experimental Validation in the Baltic Sea
The performance of this method is evaluated through realistic simulations for an algal bloom front in the Baltic sea, using the models of the AUV and the chlorophyll a sensor.
Secure Set-Based State Estimation for Linear Systems under Adversarial Attacks on Sensors
When a strategic adversary can attack multiple sensors of a system and freely choose a different set of sensors at different times, how can we ensure that the state estimate remains uncorrupted by the attacker?
Formal Verification of Linear Temporal Logic Specifications Using Hybrid Zonotope-Based Reachability Analysis
In this paper, we introduce a hybrid zonotope-based approach for formally verifying the behavior of autonomous systems operating under Linear Temporal Logic (LTL) specifications.
Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints
Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation.
A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization
The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of $n$ local cost functions by using local information exchange is considered.
Optimization and Control
How to Secure Distributed Filters Under Sensor Attacks
The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors.
Community Structure Recovery and Interaction Probability Estimation for Gossip Opinion Dynamics
The considered problem is to jointly recover the community labels of the agents and estimate interaction probabilities between the agents, based on a single trajectory of the model.
Stability Conditions for Remote State Estimation of Multiple Systems over Multiple Markov Fading Channels
We investigate the stability conditions for remote state estimation of multiple linear time-invariant (LTI) systems over multiple wireless time-varying communication channels.
Stochastic Stability of Discrete-time Phase-coupled Oscillators over Uncertain and Random Networks
We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i. e., stochastic phase-cohesiveness with respect to the origin.
Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization
This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions.
Geometrical Characterization of Sensor Placement for Cone-Invariant and Multi-Agent Systems against Undetectable Zero-Dynamics Attacks
Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected, and hence cannot be detected from the output.
A Model Randomization Approach to Statistical Parameter Privacy
To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation.
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c, 1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0, 1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results.
Event-Triggered Distributed Estimation With Decaying Communication Rate
We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate.
Multi-Layer SIS Model with an Infrastructure Network
We develop a layered networked spread model for a susceptible-infected-susceptible (SIS) pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible (SIWS) model.
Computing Complexity-aware Plans Using Kolmogorov Complexity
We present two algorithms obtaining low-complexity policies, where the first algorithm obtains a low-complexity optimal policy, and the second algorithm finds a policy maximising performance while maintaining local (stage-wise) complexity constraints.
Distributed Optimal Allocation with Quantized Communication and Privacy-Preserving Guarantees
It is shown that the proposed privacy-preserving resource allocation algorithm performs well with an appropriate convergence rate under privacy guarantees.
Finite Time Exact Quantized Average Consensus with Limited Resources and Transmission Stopping for Energy-Aware Networks
Motivated by these novel requirements, in this paper, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), and (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage).
Data-driven Set-based Estimation of Polynomial Systems with Application to SIR Epidemics
The offline phase utilizes past input-output data to estimate a set of possible coefficients of the polynomial system.
Truck Platoon Formation at Hubs: An Optimal Release Time Rule
The vehicles wait at the hub, and a platoon coordinator, at each time-step, decides whether to release the vehicles from the hub in the form of a platoon or wait for more vehicles to arrive.
Finite Time Privacy Preserving Quantized Average Consensus with Transmission Stopping
Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy.
Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks
In this paper, we focus on the problem of data sharing over a wireless computer network (i. e., a wireless grid).
Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping
The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities.
A Sample-Based Algorithm for Approximately Testing $r$-Robustness of a Digraph
In this paper, we propose a sample-based algorithm to approximately test $r$-robustness of a digraph with $n$ vertices and $m$ edges.
Distributed CPU Scheduling Subject to Nonlinear Constraints
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games
Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions.
Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors
At each time step, the local estimates of sensors are fused by solving an optimization problem to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers.
Safe Reinforcement Learning using Data-Driven Predictive Control
If there is an intersection between the reachable set of the robot using the proposed action, we call the data-driven predictive controller to find the closest safe action to the proposed unsafe action.
Distributed Optimization with Quantized Gradient Descent
For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps).
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication
We present two distributed algorithms which rely on quantized operation (i. e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps.
Hierarchical Finite State Machines for Efficient Optimal Planning in Large-scale Systems
The algorithm consists of an offline and an online step.
Rollout-Based Charging Strategy for Electric Trucks with Hours-of-Service Regulations (Extended Version)
We assume that a collection of charging and rest stations is given along a pre-planned route with known detours and that the problem data are deterministic.
Online Control Synthesis for Uncertain Systems under Signal Temporal Logic Specifications
It is shown that when the STL formula is robustly satisfiable and the initial state of the system belongs to the initial root node of the tTLT, it is guaranteed that the trajectory generated by the control synthesis algorithm satisfies the STL formula.
Policy Evaluation in Distributional LQR
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL.
Learning Flow Functions from Data with Applications to Nonlinear Oscillators
We show that the proposed architecture is able to approximate the flow function by exploiting the system's causality and time-invariance.
Efficient and Reconfigurable Optimal Planning in Large-Scale Systems Using Hierarchical Finite State Machines
In this paper, we consider a planning problem for a large-scale system modelled as a hierarchical finite state machine (HFSM) and develop a control algorithm for computing optimal plans between any two states.
Secure State Estimation with Asynchronous Measurements against Malicious Measurement-data and Time-stamp Manipulation
To deal with such attacks, we propose the design of local estimators based on observability space decomposition, where each local estimator updates the local state and sends it to the fusion center after sampling a measurement.
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence
We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning.
Universal approximation of flows of control systems by recurrent neural networks
In this paper, we prove that an architecture based on discrete-time recurrent neural networks universally approximates flows of continuous-time dynamical systems with inputs.
Parameterization-Free Observer Design for Nonlinear Systems: Application to the State Estimation of Networked SIR Epidemics
In this paper, we present an observer design approach for estimating the state of nonlinear systems, without requiring any parameterization of the system's nonlinearities.
Complexity reduction for resilient state estimation of uniformly observable nonlinear systems
As a result, although a portion of measurements are compromised, they can be locally identified and excluded from the state estimation, and thus the true state can be recovered.
What is the Expected Transient Behavior of Opinion Evolution for Two Communities?
Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between.
Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition
Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to $\mathcal{O}(\log(T))$, and the network cumulative constraint violation bound is reduced to $\mathcal{O}(\sqrt{\log(T)T})$ and $\mathcal{O}(\log(T))$ without and with Slater's condition, respectively.
Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms
We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules.
Online Distributed Learning with Quantized Finite-Time Coordination
Online distributed learning refers to the process of training learning models on distributed data sources.
Data-Driven Reachability Analysis of Pedestrians Using Behavior Modes
Previous approaches to predicting the future state sets of pedestrians either do not provide safety guarantees or are overly conservative.
Online Distributed Learning over Random Networks
The recent deployment of multi-agent systems in a wide range of scenarios has enabled the solution of learning problems in a distributed fashion.
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication
Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor.
Gain and Phase: Decentralized Stability Conditions for Power Electronics-Dominated Power Systems
This paper proposes decentralized stability conditions for multi-converter systems based on the combination of the small gain theorem and the small phase theorem.
Optimal Privacy-Aware Dynamic Estimation
In our setup, a private process, modeled as a first-order Markov chain, derives the states of the system, and the state estimates are shared with an untrusted party who might attempt to infer the private process based on the state estimates.
Almost Exact Recovery in Gossip Opinion Dynamics over Stochastic Block Models
It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities.
Learning flow functions of spiking systems
We propose a framework for surrogate modelling of spiking systems.
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems
This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems.
MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication
Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i. e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops).
Enhancing Privacy in Federated Learning through Local Training
In this paper we propose the federated private local training algorithm (Fed-PLT) for federated learning, to overcome the challenges of (i) expensive communications and (ii) privacy preservation.
Risk-averse Learning with Non-Stationary Distributions
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time.
Guaranteed Completion of Complex Tasks via Temporal Logic Trees and Hamilton-Jacobi Reachability
By ensuring a temporal logic tree has no leaking corners, we know the temporal logic tree correctly verifies the existence of control policies that satisfy the specified task.
