Welcome to the Intelligent Systems Research Laboratory at Texas A&M University. We focus on information fusion and uncertainty quantification on manifolds, for estimation and control of nonlinear dynamical systems. Below are highlights from our recent work.

Real-Time Predictive Analytics for Space Situational Awareness

Space situational awareness is concerned with tracking of space objects and classifying it with respect to certain characteristics. In this research, we are developing novel algorithms for uncertainty propagation and state estimation. Challenges include non Gaussian uncertainty supported on cylindrical coordinate systems $\mathbb{R}^5\times\mathbb{S}$, sparse sensing and unknown sensor characteristics. These algorithms are used for conjunction analysis, which is the process of predicting upcoming object encounters in an effort to notify satellite operators and avoid high risk encounters. This work is been funded by AFOSR and Intelligent Fusion Technology.

Selected Papers

  1. N. Das, V. Deshpande, R. Bhattacharya, Optimal Transport based Tracking of Space Objects using Range Data from a Single Ranging Station, AIAA JGCD, 2019.
  2. N. Das, R. Bhattacharya, Sparse Sensing Architecture for Kalman Filtering with Guaranteed Error Bound, 1st IAA Conference on Space Situational Awareness (ICSSA), Orlando, FL, USA, 2017.
  3. N. Das, R. P. Ghosh, N. Guha, R. Bhattacharya, B. K. Mallick, Optimal Transport Based Tracking of Space Objects in Cylindrical Manifolds, The Journal of Astronautical Sciences, 2019.

Asynchronous Numerical Algorithms

Future exascale machines are expected to have $10^5–10^6$ processors, providing a deep hierarchy of systems and resources. However, many challenges exist, which must be overcome before exascale systems can be utilized as an effective tool to further understanding critical scientific inquires. Among the main obstacle to scale code to exascale levels, is the communication necessary in tightly coupled problems, for example in uncertainty propagation, turbulence flow simulations at high Reynolds numbers, and large-scale convex optimization. The synchronization across processors can cause 50-80% processor idle time. In our research, we focus on asynchronous numerical algorithms that do not wait for data to be synchronized. Communication between processor is modeled as a stochastic channel, and the behaviour of the numerical algorithm is analyzed in a stochastic jump dynamical system framework. This work has been funded by NSF.

Selected Papers

  1. K. Kumari, R. Bhattacharya, D. Donzis, A Unified Approach for Deriving Optimal Finite Differences, Journal of Computational Physics, 2019.
  2. K. Lee, R. Bhattacharya, J. Dass, V. Sakuru, and R. Mahapatra, A Relaxed Synchronization Approach for Solving Parallel Quadratic Programming Problems with Guaranteed Convergence, IPDPS, Chicago,2016.
  3. K. Lee and R. Bhattacharya, On the Relaxed Synchronization for Massively Parallel Numerical Algorithms, American Control Conference, 2016.
  4. K. Lee, R. Bhattacharya, and V. Gupta, A Switched Dynamical System Framework for Analysis of Massively Parallel Asynchronous Numerical Algorithms, ACC, 2015.

Robust Cyber Physical Systems

Cyber physical systems have strong coupling between physics, communication and computation. In our research, we develop algorithms for quantifying uncertainty in system behaviour due uncertainties in the physics (unmodelled dynamics, process and sensor noise), communication (irregular channels, packet loss, etc), computation (jitter in real-time tasks, CPU transients, etc). The system level behaviour is modeled as a stochastic jump system and new uncertainty propagation algorithms for such jump systems are developed. New stochastic scheduling algorithms are developed that switch between computational tasks to ensure system-level robustness. This work has been funded by NSF.

Selected Papers

  1. K. Lee, R. Bhattacharya, Design of Resource-Optimal Switching for Resource-Constrained Dynamical Systems, International Journal of Control, Automation, and Systems, 2018.
  2. K. Lee, R. Bhattacharya, Stability Analysis of Large-Scale Distributed Networked Control Systems with Random Communication Delays: A Switched System Approach, System & Control Letters, 2015.
  3. K. Lee, A. Halder, R. Bhattacharya, Probabilistic Robustness Analysis of Stochastic Jump Linear Systems, ACC, 2014.
  4. P. Dutta, A. Halder, R. Bhattacharya, Uncertainty Quantification for Stochastic Nonlinear Systems using Perron-Frobenius Operator and Karhunen-Loeve Expansion, IEEE Multi-Conference on Systems and Control, Dubrovnik, Oct 2012.
  5. R. Bhattacharya, G. J. Balas, Control in Computationally Constrained Environments, IEEE Control Systems Technology, Volume 17, Issue 3, 2009.
  6. R. Bhattacharya, G. J. Balas, Anytime Control Algorithm: Model Reduction Approach, Journal of Guidance, Control, and Dynamics, 2004, Vol. 27, No.5, pp. 767-776, 2004.

Uncertainty Quantification in Planetary Entry, Descent, and Landing

Hypersonic flight leading to entry descent landing of a large spacecraft on the surface of Mars has been identified as a research area by NASA. The requirement is to land within a few kilometers of the robotic test sites. One of the major concerns of high mass entry is the mismatch between entry conditions and deceleration capabilities provided by supersonic parachute technologies. In such applications, there are uncertainties present in initial conditions and other system parameters. Estimation of parameters for these systems is a hard problem because of the nonlinearities in the system and the lack of frequent measurements. The evolution of uncertainty (as shown in the figure) is non Gaussian. In our work, we develop new algorithms for UQ, state-estimation, and guidance algorithms. The controlled descent ensures robustness with respect to system uncertainties, and guarantees landing at the desired site with high accuracy. This work has been funded by NASA.

Selected Papers

  1. A. Halder, R. Bhattacharya, Dispersion Analysis in Hypersonic Flight During Planetary Entry Using Stochastic Liouville Equation, AIAA Journal of Guidance, Control, and Dynamics,2011, 0731-5090 vol.34 no.2 (459-474).
  2. P. Dutta & R. Bhattacharya, Nonlinear Estimation of Hypersonic State Trajectories in Bayesian Framework with Polynomial Chaos, Journal of Guidance, Control, and Dynamics, vol.33 no.6 (1765-1778), 2011.
  3. P. Dutta & R. Bhattacharya, Hypersonic State Estimation Using Frobenius-Perron Operator, AIAA Journal of Guidance, Control, and Dynamics,Volume 34, Number 2, 2011.
  4. J. Fisher, R. Bhattacharya, Linear Quadratic Regulation of Systems with Stochastic Parameter Uncertainties, Automatica, 2009.

Unmanned Aerial Systems

Our lab has expertise in designing custom aerial platforms for various needs. Our research integrates aerodynamics, structural design and flight control design in a single unified framework. The objective is to develop next generation tools for rapid custom design of high confidence unmanned air vehicles for various industries including defense, oil & gas, and precision agriculture. The vision is to codesign much of the system engineering aspect by integrating state-of-the-art in computational fluid dynamics, structural mechanics, robust control theory, CAD software and 3D printing. The application focus is currently on aerospace systems, but can be extended to general autonomous systems.

Selected Papers

  1. S. C. Hsu, R. Bhattacharya, Design of Stochastic Collocation Based Linear Parameter Varying Quadratic Regulator, American Control Conference, 2017.
  2. A. Halder, K. Lee, and R. Bhattacharya, Optimal Transport Approach for Probabilistic Robustness Analysis of F-16 Controllers, AIAA Journal of Guidance, Control, and Dynamics, 2015.
  3. R. Bhattacharya, S. Mijanovic , E. Scholte , A. Ferrari , M. Huzmezan, M. Lelic, M. Atalla, Rigorous Design of Real-Time Embedded Control Systems, IEEE Advanced Process Control Applications for Industry, Vancouver, May, 2006.
  4. R. Bhattacharya, G. J. Balas, Implementation of Online Control Customization within the Open Control Platform, Software-Enabled Control: Information Technologies for Dynamical Systems, A John Wiley/IEEE Press Publication, 2003.
  5. R. Bhattacharya, G. J. Balas, M. Alpay Kaya, A. Packard, Nonlinear Receding Horizon Control of an F-16 Aircraft, Journal of Guidance, Control, and Dynamics, Vol. 25, No. 5, pp. 924-931, 2002.