News and Announcements

Thank you

  1. Thank you Dr. Manas Bera for inviting me for an expert talk on "Model Predictive Control with data and distribution" in the online STC & FDP, ACEOCPS-2025 organized by Dept. of Electrical Engineering, NIT Rourkela.
  2. Thanks to Dr. Amit Kumar Chowdhary and the organizers of BITCON-2024 for inviting me as a keynote speaker to talk on "Safe interaction of an agent with the environment" at the 2024 IEEE Flagship International BIT Conference-2024 (BITCON-2024) at BIT Sindri.
  3. Thanks to Prof. D. Debnath and Prof. R. S. Chakraborty for inviting me as a Meta-reviewer and session chair for IEEE INDICON 2024.
  4. Thanks to Prof. Shreya Matilal for moderating the Symposium on Epistemological Innovation Reconnaissance of the Contours of Legal Research Methodology and to Prof. Arindam Basu for inviting me as a resource person to speak on "Computational legal research and artificial intelligence".

PSALM Lab

PSALM (Predictive Stochastic Adaptive Learning Machines) Lab at IIT Kharagpur is focused on research at the intersection of control theory and Artificial Intelligence (AI). The real world applications of AI require safety guarantee and explainability of the AI module. In addition, several modules such as sensing and perception, planning and action need to interact to complete the feedback loop. Our main focus is in planning and decision making but we also consider sensing and actuation along with the effects of communication channels whenever required.

Deep MPC

This paper presents a deep learning-based model predictive control algorithm for control affine nonlinear discrete-time systems with matched and bounded state-dependent uncertainties of unknown structure. Since the structure of uncertainties is not known, a deep neural network (DNN) is employed to approximate them. In order to avoid any unwanted behavior during the learning phase, a tube-based nonlinear model predictive controller is employed, which ensures satisfaction of constraints and input-to-state stability of the closed-loop states.

Algorithmic contributions

  • We have developed several computationally tractable classes of policies.
  • A moving horizon estimator developed by us exploits control-estimation duality.
  • Our current focus is on theoretical deevelopments based on physics informed neural networks, neural operators, polynomial chaos theory.