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Found 30 papers, 0 papers with code
On Approximate Opacity of Stochastic Control Systems
Based on this new system relation, we show that one can verify opacity for stochastic control systems using their abstractions (modeled as finite gMDPs).
Secure-by-Construction Synthesis for Control Systems
These controllers are synthesized based on a concept of so-called (augmented) control barrier functions, which we introduce and discuss in detail.
Closure Certificates
A barrier certificate, defined over the states of a dynamical system, is a real-valued function whose zero level set characterizes an inductively verifiable state invariant separating reachable states from unsafe ones.
Logic in Computer Science Systems and Control Systems and Control
Abstraction-Based Verification of Approximate Pre-Opacity for Control Systems
In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder.
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems
A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems.
Constructing MDP Abstractions Using Data with Formal Guarantees
We then propose a scenario convex program (SCP) associated to the original RCP by collecting a finite number of data from trajectories of the system.
Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting
The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting.
Sandboxing (AI-based) Unverified Controllers in Stochastic Games: An Abstraction-based Approach with Safe-visor Architecture
In this paper, we propose a construction scheme for a Safe-visor architecture for sandboxing unverified controllers, e. g., artificial intelligence-based (a. k. a.
Compositional Synthesis of Signal Temporal Logic Tasks via Assume-Guarantee Contracts
In this paper, we focus on the problem of compositional synthesis of controllers enforcing signal temporal logic (STL) tasks over a class of continuous-time nonlinear interconnected systems.
Data-driven Safety Verification of Stochastic Systems via Barrier Certificates
In this paper, we propose a data-driven approach to formally verify the safety of (potentially) unknown discrete-time continuous-space stochastic systems.
Data-driven verification and synthesis of stochastic systems via barrier certificates
In this work, we study verification and synthesis problems for safety specifications over unknown discrete-time stochastic systems.
Formal Synthesis of Controllers for Uncertain Linear Systems against $ω$-Regular Properties: A Set-based Approach
Then, we compute the maximal HCI set over the state set of the product system by leveraging a set-based approach.
Compositional Abstractions of Interconnected Discrete-Time Switched Systems
Particularly, we use a notion of so-called alternating simulation function as a relation between each switched subsystem and its finite abstraction.
Compositional Verification of Initial-State Opacity for Switched Systems
In this work, we propose a compositional framework for the verification of approximate initial-state opacity for networks of discrete-time switched systems.
Sandboxing Controllers for Stochastic Cyber-Physical Systems
However, current available solutions for sandboxing controllers are just applicable to deterministic (a. k. a.
Synthesis of Partially Observed Jump-Diffusion Systems via Control Barrier Functions
In this paper, we study formal synthesis of control policies for partially observed jump-diffusion systems against complex logic specifications.
Verification of Hyperproperties for Uncertain Dynamical Systems via Barrier Certificates
These verification conditions are then discharged by synthesizing so-called augmented barrier certificates, which provide certain safety guarantees for the underlying system.
Automata-based Controller Synthesis for Stochastic Systems: A Game Framework via Approximate Probabilistic Relations
In this work, we propose an abstraction and refinement methodology for the controller synthesis of discrete-time stochastic systems to enforce complex logical properties expressed by deterministic finite automata (a. k. a.
A Lyapunov-based ISS small-gain theorem for infinite networks of nonlinear systems
In this paper, we show that an infinite network of input-to-state stable (ISS) subsystems, admitting ISS Lyapunov functions, itself admits an ISS Lyapunov function, provided that the couplings between the subsystems are sufficiently weak.
Optimization and Control 37B25, 37L15, 93D05, 93A15
ISS small-gain criteria for infinite networks with linear gain functions
This paper provides a Lyapunov-based small-gain theorem for input-to-state stability (ISS) of networks composed of infinitely many finite-dimensional systems.
Optimization and Control Dynamical Systems
Compositional Synthesis of Control Barrier Certificates for Networks of Stochastic Systems against $ω$-Regular Specifications
This paper is concerned with a compositional scheme for the construction of control barrier certificates for interconnected discrete-time stochastic systems.
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a. k. a.
Symbolic Models for Infinite Networks of Control Systems: A Compositional Approach
The proposed approach is based on the notion of alternating simulation functions.
From Small-Gain Theory to Compositional Construction of Barrier Certificates for Large-Scale Stochastic Systems
This paper is concerned with a compositional approach for the construction of control barrier certificates for large-scale interconnected stochastic systems while synthesizing hybrid controllers against high-level logic properties.
Compositional Construction of Control Barrier Functions for Continuous-Time Stochastic Hybrid Systems
In this work, we propose a compositional framework for the construction of control barrier functions for networks of continuous-time stochastic hybrid systems enforcing complex logic specifications expressed by finite-state automata.
A Framework for Output-Feedback Symbolic Control
Symbolic control is an abstraction-based controller synthesis approach that provides, algorithmically, certifiable-by-construction controllers for cyber-physical systems.
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes
This paper focuses on the controller synthesis for unknown, nonlinear systems while ensuring safety constraints.
Data-Driven Verification under Signal Temporal Logic Constraints
Since the dynamics are parameterized and partially unknown, we collect data from the system and employ Bayesian inference techniques to associate a confidence value to the satisfaction of the property.
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning
A key contribution of the paper is to leverage the classical convergence results for reinforcement learning on finite MDPs and provide control strategies maximizing the probability of satisfaction over unknown, continuous-space MDPs while providing probabilistic closeness guarantees.
dtControl: Decision Tree Learning Algorithms for Controller Representation
In particular the latter turns out to be extremely efficient, yielding decision trees with a single-digit number of decision nodes on 5 of the case studies.
