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Jayaprakash Suraj Nandiganahalli

Research interests

My research is focussed on developing real-time, intelligent algorithms for control, estimation, and safety of networked cyber physical systems. These dynamic systems interact with their environment in complex ways and involve challenges due to sub-system interactions, unknown parameter and disturbance uncertainties, and nontrivial actuator/communication lags. The goal is to develop theoretically proven controllers and inference schemes with semi-global convergence properties with known performance bounds. Applications include practical problems in aviation, human-machine systems, and other high precision systems. This involves a mix of physical modeling, adaptive control, robust control, predictive theory, Lyapunov functional analysis. Specifically, I have been working on:
 
1. Automatic Control System:
 
A. Developed a Mathematical Control framework referred to as Predictor Adaptive Robust Tracking Control (PARC) of an Input Time Delay System subject to Parametric and Time-varying Disturbance Uncertainties with Guaranteed Theoretical Performance. Applications include autonomous vehicles, flight control, precision motor control of linear motor drive system. Advantages: integrated controller that simultneously captures the effect of inherent system uncertainties and time-delay.
 
 
<Delay-tolerant Adaptive Robust Control (PARC) Framework>
 
B. Co-developed (with Omanshu Thapliyal) a Kalman Filtering Algorithm for Markovian Packet Loss & Sensor Degradation Systems with Guaranteed Convergence Performance. Applications include V2V / V2E target tracking. Advantages: state estimator that captures the effect of uncertain communication constraints and multiplicative degradation in sensor performance
<Markovian measurement matrix: full packet loss, sensor degradation, nominal>
 
2. Human-Machine Interaction:
 
A. Developed a Hybrid System theory based Formal Model Checking Tool for Automation Surprise Detection in the Flight Deck Human-Machine System using Predicate-based Intent Abstraction and Validation on Well-documented Aircraft Incidents (NASA PHASE-I / PHASE-II SBIR Award): Applications include deterministic and probabilistic formal verification of pilot-automation interaction to qualitatively and quantitatively analyze the pilot-automation interaction and validation with real world cockpit mode confusion incidents/accidents reported in the NASA Aviation Safety Reporting System (ASRS) reports, and with simulated and real flight data. Advantages: a generic Markov based formal safety analysis approach that is computationally efficient using an appropriate abstraction logic.
 
 
<Hybrid Formal VnV Framework for Anomaly Detection using Intent-based Predicate Abstraction>
 
B. Co-developed (with Hao Lyu) a Data-driven Intent Inference method using Generalized Fuzzy Hidden Markov Model for Automation Surprise Detection in the Flight Deck-Human Machine Systems. Applications include anomaly detections in the human-machine systems as in the flight-deck. Advantages: captures the system uncertainties and the vagueness in the delineation between different intent states to effeciently describe the aircraft behavior while not requiring the detailed mathematical models of the autopilot logic.
 
 
 
<Data-driven Fuzzy HMM for Intent Inference>

 

Background

January 2014-current PhD candidate (Donnan PhD Dissertation Fellowship, 2017-2018), Dept. of Aeronautics & Astronautics at Purdue University, USA.

August 2012-December 2013 MS, Dept. of Aeronautics & Astronautics at Purdue University, USA.

August 2005-May 2009 B.Tech, Dept. of Mechanical Engineering, National Institute of Technology (N.I.T.), Rourkela, India.

 

Work-Experience

December 2009- September 2010 Scientist-B in Indian Aeronautical Development Establishment (DRDO: ADE) worked on Conceptual Design and Intelligent Controller for a low-cost UAV.

Summer of 2009 DAAD Intern at Heinz Nixdorf Institute, Paderborn, Germany worked on Path-planning Algorithm for Advanced Driver Assistance System (ADAS: BMW-540 test car) incorporating vehicle dynamics and tire friction model for obstacle avoidance and goal reaching.

Summer of 2010 DAAD Intern at FH Regensburg, Germany worked on Design of Motion Kinematics for a 2D Omni-directional Trilopede (3-legged) robot.

December 2010- January 2012 DLR-DAAD Scholar at German Aerospace Center (DLR), Braunschweig, Germany worked on 3D Obstacle Avoidance Algorithms for small autonomous helicopter.

Patent

Gareth John Monkman, Daniel Wahler, and Jayaprakash Suraj Nandigahahlli, "Trilopede Mobile Robot: Omnidirectionally mobile robot having three feet", 2012, UK patent published GB2486013.

Conference Proceedings:

1. M. Schumm, E. Schwarz, D. Wahler, S.N. Jayaprakash and G.J. Monkman, "The Trilopede," Informatics Microsystems Information Systems Preceedings, Vol 1 (1), Moscow State Technical University, 2012.

2. S. Shin, J. S. Nandiganahalli, and I. Hwang, "Diagnostic Tool for Throughput Factor Analysis in En-route Airspace," In the Proceedings of the 2013 Aviation Technology, Integration, and Operations (ATIO) Conference, Los Angeles, CA, 2013.

3. J. S. Nandiganahalli, S. Lee, I. Hwang and B. J. Yang, "User Interface Validation using Mode Confusion Detection," In the Proceedings of the 2014 Aviation Technology, Integration, and Operations (ATIO) Conference, Atlanta, GA, 2014.

4. J. S. Nandiganahalli, H. Lyu and I. Hwang, "Formal Extensions to the Intent-based Mode Confusion Detection Framework," In the Proceedings of the 2015 Aviation Technology, Integration, and Operations (ATIO) Conference,Dallas, TX, 2015

5. J.S. Nandiganahalli, S. Lee, and I. Hwang, "Intent-based Abstraction for Formal Verification of Flight Deck Mode Confusion," AIAA SciTech 2016: AIAA Infotech@Aerospace, San Diego, CA, January 2016 (Best Graduate Student Paper Award for "Intelligent Systems: Human-Machine Interaction" Student Paper Competition).

6. J.S. Nandiganahalli, S. Lee, and I. Hwang, "Flight Deck Mode Confusion Detection using Intent-based Probabilistic Model Checking," AIAA SciTech 2017: AIAA Infotech@Aerospace, Grapevine, TX, January 2017 (Finalist for the Best Graduate Student Paper Award for "Intelligent Systems: Human-Machine Interaction" - 3rd place).

7. H. Lyu, J. S. Nandiganahalli, and I. Hwang, "Human Automation Interaction Issue Detection Using a Generalized Fuzzy Hidden Markov Model," AIAA SciTech 2017: AIAA Infotech@Aerospace, Grapevine, TX, January, 2017 (Finalist for the Best Graduate Student Paper Award for "Intelligent Systems: Human-Machine Interaction" - 2nd place).

8. J.S. Nandiganahalli, C. Kwon, I. Hwang, "Delay-tolerant Adaptive Robust Compensation for Flight Control," AIAA SciTech 2018: AIAA Infotech@Aerospace, Kissimmee, FL, USA, January 2018 (Finalist for the Best Graduate Student Paper Award "Intelligent Systems: Adaptive and Intelligent Control Systems").

 

Journals:

1. S. Shin, J. S. Nandiganahalli, J. Wei, and I. Hwang, "Diagnostic for Throughput Factor Analysis in En-route Airspace," Journal of Aircraft (JOA), Vol. 53, No. 3, pp. 665-679, 2016.

2. J. S. Nandiganahalli, S. Lee, and I. Hwang, "Formal Verification for Mode Confusion in the Flight Deck using Intent-based Abstraction," Journal of Aerospace Information Systems (JAIS), Vol. 13, No. 9, pp. 343-356, 2016.

3. H. Lyu, J. S. Nandiganahalli, and I. Hwang, "Intent-based Learning Method for Flight Deck Human-Automation Interaction Issue Detection," Accepted to the Journal of Aerospace Information Systems (JAIS), 2017.

4. Few more are under review ...(will be updated soon)

 

Contact

jnandiga@purdue.edu


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