Advanced Controls and Sensors Group
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Technical Lead: F.L. Lewis, Ph.D.
Moncrief-Donnell Endowed Chair F.L.
Lewis Professional Details-
PhD Students EE "Systems
and Controls" Courses and Notes |
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Research Areas: SEE RECENT PRESENTATIONS BELOW Cooperative Control of Distributed Systems on Graphs Cooperative control of Renewable Energy Microgrids Reinforcement Learning & Approximate Dynamic Programming See recent presentations below ADP for continuous-time systems Human-Robot Interactions Intelligent Nonlinear Control Neural
network control of robots and nonlinear systems Hamilton Jacobi equation solution using neural networks Optimal control for nonlinear systems H-infinity (game theory) control Discrete Event Supervisory Control Intelligent Diagnostics and Prognostics Software from sponsored research |
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MAIN BOOKS
F.L. Lewis, D. Vrabie, and V. Syrmos, Optimal Control, third edition, John Wiley and Sons, New York, 2012.
F.L. Lewis, L. Xie, and D. Popa, Optimal & Robust Estimation: With an Introduction to Stochastic Control Theory, CRC Press, Boca Raton, 2007. Second Edition.
B.L. Stevens,
F.L. Lewis, E.N. Johnson, Aircraft Control and Simulation: Dynamics,
Control, and Autonomous Systems, John Wiley and Sons, New York, Feb. 1992.
Third edition 2015.
http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118870980.html
D. Vrabie, K. Vamvoudakis, and F.L. Lewis, Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles, IET Press, 2012.
F.L. Lewis and Derong Liu, editors, Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, John Wiley/IEEE Press, Computational Intelligence Series. 2012.
F.L. Lewis, H. Zhang, K. Hengster-Movric, A. Das, Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches, Springer-Verlag, 2014.
F.L. Lewis,
S. Jagannathan, and A. Yesildirek, Neural Network
Control of Robot Manipulators and Nonlinear Systems, Taylor and Francis,
London, 1999.
download
pdf file
F.L. Lewis, Applied Optimal Control and Estimation: Digital Design and Implementation, Prentice-Hall, New Jersey, TI Series, Feb. 1992.
F.L. Lewis,
D.M. Dawson, and C.T. Abdallah, Robot Manipulator Control: Theory and
Practice, 2nd edition, Revised and Expanded, CRC Press, Boca
Raton, 2006.
download
pdf file
Y. Kim and F.L. Lewis, High-Level Feedback Control with Neural Networks," World Scientific, Singapore, 1998.
G. Vachtsevanos, F.L. Lewis, M. Roemer, A. Hess, B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley, New York, 2006.
PRESENTATIONS IN MAIN RESEARCH AREAS
Workshop on Reinforcement Learning 2018
Main Background Development for
Integral Reinforcement Learning
Applications of
IRL- Microgrids, UAV, Human-Robot Interaction
Reinforcement Learning for Continuous
Systems Optimality and Games
Reinforcement Learning for Discrete-time Systems
RL for Data-driven Optimization and Supervisory Process
Control
Networked Multi-agent Systems
Control- Stability vs. Optimality, and Graphical Games
Output Regulation of Heterogeneous
MAS- Reduced-order design and Geometry
Cooperative Synchronization Control for AC Microgrids
Adaptive, Robust, Neural Network Control for Robots and Nonlinear Systems
Condition-Based Maintenance and Prognostics for Health Management
Discrete-Event Control for Industrial Processes
OLDER INVITED PRESENTATIONS
Various
invited talks 2015, Integral
Reinforcement Learning for Real-time Optimal Control and Differential
Multi-player Gamesť
Keynote
Speaker, Int. Symposium on Resilient Control Systems, Philadelphia, August
2015, Reinforcement
Learning for Resilient Control in Cooperative and Adversarial Multi-agent Networks:
CPS Applications in Microgrid and Human-Robot Interactions
Invited
Talk, Carnegie Mellon Pacific Campus, NASA Ames, Cal, April 2015, Cooperative Control
for Renewable Energy Microgrids
Opening
Invited Speaker, Workshop on Robotics and Biotechnology, Hong Kong City University,
16 Jan. 2015, “Reinforcement learning
for human-robot interaction”
Invited
Talk, Northeastern University, Shenyang,, China, Jan.
2015, Data-driven
Optimization and Supervisory Control for Industrial Processes
Optimal
Control, Workshop at Northeastern University, Shenyang, China, 5-6 November
2014. Qian Ren and Project 111 Program.
download
pdf file, opfb H-infinity paper
Data-driven Control and
Optimization for Industrial Processes, Workshop at Northeastern University,
Shenyang, China, May 2014. Qian Ren and Project 111 Program.
Reinforcement
Learning and ADP for Real-Time Optimal Control and Dynamic Games, Plenary
Talk, Int. Joint Conference on Neural Networks, Dallas, August 2013
Data-driven Control and Optimization
for Industrial Processes: Reinforcement Learning & Supervisory Control,
Workshop at Northeastern University, Shenyang, China, July 2013. Project
111 Program.
Optimal
Distributed Cooperative Control of Multi-Agent Systems and Graphical Games,
Plenary Talk, Int. Conf. Intelligent Control and
Information Processing ICICIP, Beijing, June 2013.
Distributed
Cooperative Control for Electric Power Microgrid Applications, Plenary
Talk, IEEE CYBER, Nanjing, May 2013.
Reinforcement
Learning Adaptive Structures for Real-Time Optimal Control and Graphical Games,
Invited Talk, Chinese University of Hong Kong, May 2013.
Adaptive
Tuning for Optimal Process Control and Multi-Process Games Using Reinforcement
Learning, Singapore Institute of Manufacturing Technology SIMTech, May
2013.
Optimal
Adaptive Control Using Reinforcement Learning, Opening Plenary Talk, IEEE
Multi-Conference on Systems and Controls, Dubrovnik, Croatia, Oct. 2012.
Novel
Adaptive Control Structures by Reinforcement Learning, Opening Plenary Talk,
Int. Conf. on System Theory, Control and Computing, Sinaia,
Romania, Oct. 2012.
Reinforcement
Methods for Online Learning in Autonomous Robotic Systems, Plenary Talk,
FIRA Robo World Congress, Bristol, UK, 20 August 2012.
Cooperative
Control: Stability versus global optimality, Chinese Academy of Sciences,
2012
Cooperative
control: optimal design and Graphical Games, Chinese Academy of Sciences,
2012
Cooperative
Control: Optimal Design, Observers, distributed adaptive control, Chinese
Academy of Sciences, 2011
CDC Orlando 2011 workshop
Optimal Adaptive
Control: Online Solutions for Optimal Feedback Control and Differential Games Using
Reinforcement Learning
Lewis
notes- MDP and reinforcement learning
Busoniu notes
Jagannathan
notes
Vrabie notes
Lewis
notes- online synchronous policy iteration
Optimal
Control and Online Game Solutions Using Approximate Dynamic Programming,
Workshop, Symp. ADP/RL, Paris, April 2011.
UTA
Workshop on Building a Successful Research program and Mentoring PhD Students,
15 October, 2010.
Online Optimal
Adaptive Control: Real-Time learning of optimal control and zero-sum game
solutions, Plenary Talk, Chinese Conf. Decision & Control, Xuzhou, May
2010.
"Distributed
Adaptive Control for Synchronization of Unknown Nonlinear Networked Systems,” Invited Talk, 9th Symposium on Frontier Problems
in System and Control, Chinese Academy of Sciences, Beijing, May 2010
Structural Health Monitoring for
Aircraft Skin Systems, A-Star Data Storage Institute DSI, Singapore, August
2009.
INFORMATION FOR NEW STUDENTS APPLYING. Apply through Graduate Adviser, UTA Dept. of Electrical Engineering
Ph.D. Students:
· Jim Worsham, “Aircraft Autopilot Controller Design,” PhD
Funded by Lockheed Martin, Sept 2021-present.
· Yusuf Kartal, Autonomous
Aerial Vehicles Distributed Control and Interactive Games, Department
of Electrical Engineering, University of Texas at Arlington, in progress. Sept.
2019 – present.
· Bosen Lian, Distributed
Estimation and Inverse Reinforcement Learning for Multi-agent Systems, PhD
degree, Department of Electrical Engineering, University of Texas at Arlington,
in progress. Dec. 2021.
Recent Former Ph.D.
Students:
- Shan Zuo Susan, Distributed
Resilient Control Of Multi-Agent Systems With Applications
to Microgrids, Lewis is coadviser. Main Adviser
is Ali Davoudi.
- Victor Lopez, Optimal
Control Strategies for Dynamical Multiagent Systems in Graphical Games
- Patrik Kolaric,
Decision & Reinforcement Learning Control for multi-agent autonomous
systems
- Bakur ALQaudi- Brain
functions, Adaptive Resonance Theory, and Their Use in Decision and
Control Systems,
- Bahare Kiumarsi, reinforcement
learning and biologically inspired control
- Hamidreza Modares,
reinforcement learning for feedback control
- Vahidreza Nasirian,
Revisiting established control paradigms in emerging energy hubs (Coadvised. Main
adviser is Dr. Ali Davoudi, EE, UTA)
- Ali Bidram,
cooperative control for electric power microgrid (Coadvised.
Main adviser is Dr. Ali Davoudi, EE, UTA)
- Kristian
Hengster-Movric, cooperative control systems, distributed optimal design
on graphs
- Muhammad Aurangzeb,
Coalitions and Games on Graphs
- Mohammed Abouheaf,
cooperative graphical games, reinforcement learning for power system
economic dispatch
- Kyriakos Vamvoudakis,
neural networks for feedback control
- Emanuel Stingu, autonomous aerial vehicles, helicopters
- Draguna Vrabie, Neural
Networks for Control- Approximate Dynamic Programming for continuous-time
systems
- Abhijit Das, nonlinear
autopilots for UAV helicopters
- Prasanna Ballal,
Wireless Sensor Networks
- Pritpal Dang,
Man-Machine Interface
- J. Gadewadikar,
H-infinity control, output feedback control, helicopter UAV control
- Asma AL-Tamimi,
Approximate Dynamic Programming for discrete-time systems
- Cheng Tao, finite
horizon optimal control for nonlinear systems
- M. Abu-Khalaf,
Nonlinear Control and HJB Equation Design
- B. Borovic,
Microelectromechanical systems (MEMS)
- J. Mireles,
Intelligent Robot Control, Microelectromechanical systems (MEMS)
Research
Supported by (Past and Present):
National Science
Foundation
Office of Naval
Research
Army Research Office,
Army National Automotive Center, TARDEC/RDECOM
Air Force Office of
Scientific Research
ONR,
NASA, and ARO SBIR Contracts
