Ahan Basu

Ahan Basu, Ratnangshu Das, Soumyodipta Nath et al.

This paper presents a Spatiotemporal Tube (STT)-based control framework for general unknown nonlinear Euler-Lagrange (EL) systems subject to input constraints, with the objective of satisfying Signal Temporal Logic (STL) specifications, where confinement of the system trajectory within the STT guarantees the satisfaction of the corresponding STL task. For both single and multi-agent scenarios, the STT corresponding to each agent is modeled as a time-varying ball, whose center and radius are jointly parameterized using a physics-informed neural network (PINN). The robustness metric associated with the STL specification corresponding to the agents is incorporated into the training process as a loss function, enabling the learned tube to encode task-level temporal requirements. For a multi-agent scenario, we introduce an additional robustness metric corresponding to the global task, which, when satisfied, ensures the tubes do not collide with each other. To ensure that the system trajectory remains within the learned STT and thereby satisfies the local and global STL specifications, we propose a control strategy that explicitly accounts for input constraints. In particular, a closed-form control law is developed to keep the trajectory inside the tube while regulating the motion of the tube by enforcing bounds on its evolution depending on the input constraints of the system. The proposed approach has been validated over several case studies.

Ratnangshu Das, Ahan Basu, Christos Verginis et al.

This paper presents a computationally lightweight and robust control framework for differential-drive mobile robots with dynamic uncertainties and external disturbances, guaranteeing the satisfaction of Temporal Reach-Avoid-Stay (T-RAS) specifications. The approach employs circular spatiotemporal tubes (STTs), characterized by smoothly time-varying center and radius, to define dynamic safe corridors that guide the robot from the start region to the goal while avoiding obstacles. In particular, we first develop a sampling-based synthesis algorithm to construct a feasible STT that satisfies the prescribed timing and safety constraints with formal guarantees. To ensure that the robot remains confined within this tube, we then analytically design a closed-form control that is computationally efficient and robust to disturbances. The proposed framework is validated through simulation studies on a differential-drive robot and benchmarked against state-of-the-art methods, demonstrating superior robustness, accuracy, and computational efficiency.

Pukhrambam Akash Singh, Ratnangshu Das, Ahan Basu et al.

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 closed-loop methods either rely on accurate patient-specific models or do not provide formal safety guarantees, and are often computationally demanding for wearable devices. This paper proposes Glycemic Safety Tube Control (GSTC), a model-free and computationally efficient control framework for automated insulin delivery. The method enforces clinically relevant safety bounds on glucose levels by design, ensuring that glucose remains within a prescribed safe range. We also derive feasibility conditions that guarantee safety and input constraint satisfaction under bounded meal disturbances and estimation errors. The performance of GSTC is evaluated against state-of-the-art methods, including linear and nonlinear model predictive control and sliding mode control. The results demonstrate that GSTC maintains safety under varying meal patterns and patient conditions, highlighting its robustness and computational efficiency. Overall, GSTC provides a safe, efficient, and patient-independent approach for next-generation artificial pancreas systems.