4 citations · 5 across the 12 of their papers we have counts for
8 papers · 1 filter
Learning Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks using Physics-Informed Neural Networks
Ahan Basu, Ratnangshu Das, Pushpak Jagtap
This paper presents a Spatiotemporal Tube (STT)-based control framework for general control-affine MIMO nonlinear pure-feedback systems with unknown dynamics to satisfy prescribed…
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…
Temporal Reach-Avoid-Stay Control for Differential Drive Systems via Spatiotemporal Tubes
Ratnangshu Das, Ahan Basu, Christos Verginis +1
This paper presents a computationally lightweight and robust control framework for differential-drive mobile robots with dynamic uncertainties and external disturbances, guaranteei…
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…