28 citations · 82 across the 18 of their papers we have counts for
18 papers
Patching Control Lyapunov Barrier Functions for Temporal Logic Specifications with Bounded Controls
Ruikun Zhou, Yating Yuan, Haocheng Chang +2
We propose an abstraction-free framework for controller synthesis for continuous-time dynamical systems subject to Linear Temporal Logic (LTL) specifications and bounded control in…
Safe Domains of Attraction for Discrete-Time Nonlinear Systems: Characterization and Verifiable Neural Network Estimation
Mohamed Serry, Haoyu Li, Ruikun Zhou +2
Analysis of nonlinear autonomous systems typically involves estimating domains of attraction, which have been a topic of extensive research interest for decades. Despite that, accu…
Stability of Jordan Recurrent Neural Network Estimator
Avneet Kaur, Ruikun Zhou, Jun Liu +1
State estimation refers to determining the states of a dynamical system that starts from a noisy initial condition and evolves under process noise, based on noisy measurements and…
Data-driven optimal control of unknown nonlinear dynamical systems using the Koopman operator
Zhexuan Zeng, Ruikun Zhou, Yiming Meng +1
Nonlinear optimal control is vital for numerous applications but remains challenging for unknown systems due to the difficulties in accurately modelling dynamics and handling compu…
Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems
Ruikun Zhou, Yiming Meng, Zhexuan Zeng +1
Koopman operator theory has gained significant attention in recent years for identifying discrete-time nonlinear systems by embedding them into an infinite-dimensional linear vecto…
Resolvent-Type Data-Driven Learning of Generators for Unknown Continuous-Time Dynamical Systems
Yiming Meng, Ruikun Zhou, Melkior Ornik +1
A semigroup characterization, or equivalently, a characterization by the generator, is a classical technique used to describe continuous-time nonlinear dynamical systems. In the re…
Formally Verified Physics-Informed Neural Control Lyapunov Functions
Jun Liu, Maxwell Fitzsimmons, Ruikun Zhou +1
Control Lyapunov functions are a central tool in the design and analysis of stabilizing controllers for nonlinear systems. Constructing such functions, however, remains a significa…
Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems
Thanin Quartz, Ruikun Zhou, Hans De Sterck +1
Designing a stabilizing controller for nonlinear systems is a challenging task, especially for high-dimensional problems with unknown dynamics. Traditional reinforcement learning a…
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series
Ruikun Zhou, Wail Gueaieb, Davide Spinello
We propose a Kullback-Leibler Divergence (KLD) filter to extract anomalies within data series generated by a broad class of proximity sensors, along with the anomaly locations and…
LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction
Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1
In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis. The proposed tool,…
Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems
Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1
While there has been increasing interest in using neural networks to compute Lyapunov functions, verifying that these functions satisfy the Lyapunov conditions and certifying stabi…
Koopman-Based Learning of Infinitesimal Generators without Operator Logarithm
Yiming Meng, Ruikun Zhou, Melkior Ornik +1
The Koopman operator has gained significant attention in recent years for its ability to verify evolutionary properties of continuous-time nonlinear systems by lifting state variab…
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Yiming Meng, Ruikun Zhou, Amartya Mukherjee +3
Solving nonlinear optimal control problems is a challenging task, particularly for high-dimensional problems. We propose algorithms for model-based policy iterations to solve nonli…
Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1
We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions. We encode Lyapunov conditions as a partial differential equation (PDE…
Learning Regions of Attraction in Unknown Dynamical Systems via Zubov-Koopman Lifting: Regularities and Convergence
Yiming Meng, Ruikun Zhou, Jun Liu
The estimation for the region of attraction (ROA) of an asymptotically stable equilibrium point is crucial in the analysis of nonlinear systems. There has been a recent surge of in…
Harmonic Control Lyapunov Barrier Functions for Constrained Optimal Control with Reach-Avoid Specifications
Amartya Mukherjee, Ruikun Zhou, Haocheng Chang +1
This paper introduces harmonic control Lyapunov barrier functions (harmonic CLBF) that aid in constrained control problems such as reach-avoid problems. Harmonic CLBFs exploit the…
Towards Learning and Verifying Maximal Neural Lyapunov Functions
Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1
The search for Lyapunov functions is a crucial task in the analysis of nonlinear systems. In this paper, we present a physics-informed neural network (PINN) approach to learning a…
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
Ruikun Zhou, Thanin Quartz, Hans De Sterck +1
Learning for control of dynamical systems with formal guarantees remains a challenging task. This paper proposes a learning framework to simultaneously stabilize an unknown nonline…