7 papers · 1 filter
Learning Robust Control Lyapunov Functions through Lipschitz Neural Networks
Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
This work presents a novel framework for learning robust control Lyapunov functions and stabilizing controllers for nonlinear dynamical systems subject to additive disturbances upp…
Sandbox-Enabled Digital Twin for Cyber-Physical Systems
Meet Udeshi, Md Raz, Prashanth Krishnamurthy +2
Firmware/software in cyber-physical system (CPS) embedded devices/controllers can have vulnerabilities stemming from multiple sources such as weak security practices, outdated libr…
A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
Ali Umut Kaypak, Prashanth Krishnamurthy, Farshad Khorrami
Ensuring safety under unknown and stochastic dynamics remains a significant challenge in reinforcement learning (RL). In this paper, we propose a model predictive control (MPC)-bas…
Data-Efficient System Identification via Lipschitz Neural Networks
Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
Extracting dynamic models from data is of enormous importance in understanding the properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of neur…
Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions
Shiqing Wei, Rooholla Khorrambakht, Prashanth Krishnamurthy +2
Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method…
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems
Hao Fu, Prashanth Krishnamurthy, Farshad Khorrami
Cyber-physical systems (CPS) play a pivotal role in numerous critical real-world applications that have stringent requirements for safety. To enhance the CPS resiliency against att…