Diagnosis and prognosis are needed for condition-based maintenance to keep industrial machines running efficiently with reduced energy consumption and heat footprint. Industrial controllers must keep machines functioning properly and operating within tight tolerance limits. However, practical industrial and robotic systems have unknown disturbances, unmodeled dynamics, actuator constraints, friction, and restricted availability of measurements. The complex relationships between input variables such as motor speeds and output variables such as drill bit position, cutting force, and removal rate cannot usually be described by analytic expressions. Such systems cannot effectively be controlled using standard adaptive or robust feedback control techniques. The design of controllers based on measured historical data has the potential to improve performance, yet is time consuming and difficult. Nonlinear learning systems have the capability to capture complicated unknown dynamics and disturbances, and to discover the structure and input/output relationships hidden in massive data sets of historical data. One class of such systems are neural networks, which have desirable properties including classification, clustering, and approximation capabilities.
This talk has three topics. First, we will outline some applications of neural networks as classifiers in prognostics and health monitoring. Then, we will describe a family of feedback controllers that can confront industrial systems using neural networks as the basic control block structure. The learning abilities of neural networks considered as Intelligent Systems allow these controllers to learn on-line and improve their performance through tuning of the weights. These neural network controllers are tuned on-line in real time based on the system errors. Some basics of Iterative Learning Control are given. ILC updates the controller parameters only after a complete run has been done. It is suitable for run-to-run control and can discover through learning mechanisms the complex relations between input control variables and output performance variables.
2.30pm – 2.55pm Registration
3.00pm – 4.00pm Presentations by Prof Frank Lewis
4.00pm – 4.30pm Networking & Refreshment
About the Speaker
F L Lewis, Fellow IEEE, Fellow IFAC, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer, is Distinguished Scholar Professor and Moncrief-O’Donnell Chair at University of Texas at Arlington’s Automation & Robotics Research Institute. He obtained the Bachelor's Degree in Physics/EE and the MSEE at Rice University, the MS in Aeronautical Engineering from Univ. W. Florida, and the Ph.D. at Ga. Tech. He works in feedback control, intelligent systems, and sensor networks. He is author of 6 U.S. patents, 209 journal papers, 328 conference papers, 12 books, 41 chapters, and 11 journal special issues. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, and Int. Neural Network Soc. Gabor Award 2008. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the year by Ft. Worth IEEE Section. Listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. He was appointed to the NAE Committee on Space Station in 1995. He is an elected Guest Consulting Professor at both South China University of Technology and Shanghai Jiao Tong University. Founding Member of the Board of Governors of the Mediterranean Control Association. Helped win the IEEE Control Systems Society Best Chapter Award (as Founding Chairman of DFW Chapter), the National Sigma Xi Award for Outstanding Chapter (as President of UTA Chapter), and the US SBA Tibbets Award in 1996 (as Director of ARRI’s SBIR Program).
Who Should Attend
R&D managers, researchers, scientists, engineers, academic staff and engineering students.
Registration for this event is free of charge. Seats are available on a first-come, first served basis.
For technical enquiries, please contact: Ms Zhou Junhong, Email: jzhou@SIMTech.a-star.edu.sg;
Tel: 6793 8289
For general enquiries, please contact: Connie Bah, Email: email@example.com; Tel: 6793 8318