4 citations · 5 across the 12 of their papers we have counts for
10 papers · 1 filter
Fast and Sample Efficient Safety Verification via Extreme Learning Machine
Ahan Basu, Mahathi Anand, Pushpak Jagtap
Deep learning methods like neural networks have greatly simplified the computation of safety certificates for complex nonlinear systems with unknown dynamics. However, due to the d…
Glycemic Safety Tube: A Provably Safe Control Framework for Artificial Pancreas Systems under Parametric Uncertainty
Pukhrambam Akash Singh, Ratnangshu Das, Ahan Basu +1
Type 1 diabetes eliminates the body's ability to produce insulin, making glucose regulation entirely dependent on external insulin delivery and the control algorithm. Existing clos…
Input-to-State Stabilizing Neural Controllers for Unknown Switched Nonlinear Systems within Compact Sets
Bhabani Shankar Dey, Ahan Basu, Pushpak Jagtap
This paper develops a neural network based control framework that ensures system safety and input-to-state stability (ISS) for general nonlinear switched systems with unknown dynam…
Scalable Formal Verification of Incremental Stability in Large-Scale Systems Using Graph Neural Networks
Ahan Basu, Mahathi Anand, Pushpak Jagtap
This work proposes a novel distributed framework for verifying the incremental stability of large-scale systems with unknown dynamics and known interconnection structures using gra…
Spatiotemporal Tubes based Control of Unknown Multi-Agent Systems for Temporal Reach-Avoid-Stay Tasks
Ahan Basu, Ratnangshu Das, Pushpak Jagtap
The paper focuses on designing a controller for unknown dynamical multi-agent systems to achieve temporal reach-avoid-stay tasks for each agent while preventing inter-agent collisi…
Neural Network-based Co-design of Output-Feedback Control Barrier Function and Observer with Input Constraints
Vaishnavi Jagabathula, Ahan Basu, Pushpak Jagtap
Control Barrier Functions (CBFs) provide a powerful framework for ensuring safety in dynamical systems. However, their application typically relies on full state information, which…
Neural Controller for Incremental Stability of Unknown Continuous-time Systems
Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap
This work primarily focuses on synthesizing a controller that guarantees an unknown continuous-time system to be incrementally input-to-state stable (-ISS). In this context, the…
Formally Verified Neural Network Controllers for Incremental Input-to-State Stability of Unknown Discrete-Time Systems
Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap
This work aims to synthesize a controller that ensures that an unknown discrete-time system is incrementally input-to-state stable (-ISS). In this work, we introduce the notion…
Formally Verified Neural Lyapunov Function for Incremental Input-to-State Stability of Unknown Systems
Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap
This work presents an approach to synthesize a Lyapunov-like function to ensure incrementally input-to-state stability (-ISS) property for an unknown discrete-time system. To de…
Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks in Unknown Systems
Ratnangshu Das, Ahan Basu, Pushpak Jagtap
The paper considers the controller synthesis problem for general MIMO systems with unknown dynamics, aiming to fulfill the temporal reach-avoid-stay task, where the unsafe regions…