28 citations · 70 across the 25 of their papers we have counts for
15 papers · 1 filter
Physics-Informed Koopman Learning for Data-Efficient System Identification of Automated Vehicle Dynamics
Hangyu Li, Yiming Meng, Zhaohui Liang +8
Understanding how a particular automated vehicle reacts to nearby traffic can improve motion prediction, control development, traffic simulation, and safety evaluation. However, co…
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…
Koopman Spectral Analysis and System Identification for Stochastic Dynamical Systems via Yosida Approximation of Generators
Jun Zhou, Yiming Meng, Jun Liu
System identification and Koopman spectral analysis are crucial for uncovering physical laws and understanding the long-term behaviour of stochastic dynamical systems governed by s…
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…
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…
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…
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…
Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations
Chuanzheng Wang, Yiming Meng, Jun Liu +1
Control barrier functions are widely used to synthesize safety-critical controls. However, the presence of Gaussian-type noise in dynamical systems can generate unbounded signals a…
Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems
Yiming Meng, Jun Liu
The essential step of abstraction-based control synthesis for nonlinear systems to satisfy a given specification is to obtain a finite-state abstraction of the original systems. Th…
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions
Chuanzheng Wang, Yiming Meng, Stephen L. Smith +1
Control barrier functions are widely used to synthesize safety-critical controls. The existence of Gaussian-type noise may lead to unsafe actions and result in severe consequences.…
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions
Chuanzheng Wang, Yiming Meng, Stephen L. Smith +1
Control barrier functions have been widely used for synthesizing safety-critical controls, often via solving quadratic programs. However, the existence of Gaussian-type noise may l…
Learning Control Barrier Functions with High Relative Degree for Safety-Critical Control
Chuanzheng Wang, Yinan Li, Yiming Meng +2
Control barrier functions have shown great success in addressing control problems with safety guarantees. These methods usually find the next safe control input by solving an onlin…
Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specifications
Yiming Meng, Yinan Li, Maxwell Fitzsimmons +1
Stability and safety are two important aspects in safety-critical control of dynamical systems. It has been a well established fact in control theory that stability properties can…