Search Results for author:
Found 20 papers, 5 papers with code
A Framework for Safe Probabilistic Invariance Verification of Stochastic Dynamical Systems
Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications.
Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems
In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances.
UR4NNV: Neural Network Verification, Under-approximation Reachability Works!
However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question.
A New Framework for Bounding Reachability Probabilities of Continuous-time Stochastic Systems
This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs).
Safe Exit Controllers Synthesis for Continuous-time Stochastic Systems
To begin, we present a sufficient condition for establishing lower bounds on the exit probability in the first case.
Reach-avoid Analysis for Sampled-data Systems with Measurement Uncertainties
Digital control has become increasingly prevalent in modern systems, making continuous-time plants controlled by discrete-time (digital) controllers ubiquitous and crucial across industries, including aerospace, automotive, and manufacturing.
Correct-by-Construction for Hybrid Systems by Synthesizing Reset Controller
Controller synthesis, including reset controller, feedback controller, and switching logic controller, provides an essential mechanism to guarantee the correctness and reliability of hybrid systems in a correct-by-construction manner.
Verifying Safety of Neural Networks from Topological Perspectives
Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations.
Repairing Deep Neural Networks Based on Behavior Imitation
For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors.
Provable Reach-avoid Controllers Synthesis Based on Inner-approximating Controlled Reach-avoid Sets
In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set.
Reach-avoid Controllers Synthesis for Safety Critical Systems
Consequently, a lax control guidance-barrier function is further developed such that only the safe set set is an invariance before the system enters the target set, expanding the space of admissible control inputs.
Reachability Verification for Stochastic Discrete-time Dynamical Systems
In this paper we study reachability verification problems of stochastic discrete-time dynamical systems over the infinite time horizon.
Reach-avoid Verification using Lyapunov Densities
We propose two novel sufficient conditions using Lyapunov densities for the weak reach-avoid verification.
Credit Assignment for Trained Neural Networks Based on Koopman Operator Theory
Based on Koopman operator theory, this paper presents an alternative perspective of linear dynamics on dealing with the credit assignment problem for trained neural networks.
Safety Verification for Neural Networks Based on Set-boundary Analysis
The homeomorphism property exists in some widely used NNs such as invertible NNs.
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
Switching Controller Synthesis for Delay Hybrid Systems under Perturbations
Delays are ubiquitous in modern hybrid systems, which exhibit both continuous and discrete dynamical behaviors.
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning
It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN.
Improving Neural Network Verification through Spurious Region Guided Refinement
The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons.
PAC Model Checking of Black-Box Continuous-Time Dynamical Systems
In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning.
Systems and Control Formal Languages and Automata Theory Systems and Control
