Title: CPS Driven Control System

SpeakerTianyou Chai, Member of Chinese Academy of Engineering

Northeastern UniversityChina



AbstractChina has abundance of mineral resources such as magnesite, hematite and bauxite, which constitute a key component of its economy. The relatively low grade, and the widely varying andcomplex compositions of the raw extracts, however, pose difficult processing challenges including specialized equipment with excessive energy demands. The energy intensive furnaces together with widely uncertain features of the extracts form hybrid complexities of the system, where the existing modeling, optimization and control methods have met only limited success. Currently, the mineral processing plants generally employ manual control and are known to impose greater demands on the energy, while yielding unreasonable waste and poor operational efficiency. The recently developed Cyber-Physical System (CPS) provides a new key for us to address these challenges. The idea is to make the control system of energy intensive equipment into a CPS, which will lead to a CPS drivencontrol system.

This talk presents the syntheses and implementation of a CPS drivencontrol system for energy-intensive equipment under the framework of CPS. The proposed CPS drivencontrol system consists of four main functions: (I)setpoint control; (II) tracking control; (III) self-optimized tuning; and (IV)remote and mobile monitoring for operating condition. The key in realizing the above functions is the integrated optimal operational control methods to implement setpoint control, tracking control and self-optimized tuning together seamlessly.  This talk introduces the integrated optimal operational control methods we proposed.

Hardware and software platform of CPS drivencontrol system for energy-intensive equipment is then briefly introduced, which adopts embedded control system, wireless network and industrial cloud. It not only realizes the functions of computer control system using DCS (PLS), optimization computer and computer for abnormal condition identification and self-optimized tuning, but also achieves the functions of mobile and remote monitoring for industrial process.

Then, using fused magnesium furnace as an example, a hybrid simulation system for CPS drivencontrol system for energy-intensive equipment developed by our team is introduced. The results of simulationexperiments show the effectiveness of the proposed method that integrates thesetpoint control, tracking control, self-optimized tuning and remote and mobile monitoring for operating condition in the framework of CPS.

The industrial application of the proposed CPS drivencontrol system is also discussed. It has been successfully applied to the largest magnesia production enterprise in China, resulting in great returns. Finally, future research on theCPS driven control system is outlined.

Biography:

TianyouChai received thePh.D. degree in control theory and engineeringin 1985 fromNortheastern University, Shenyang, China, where he became a Professor in 1988.He is the founder and Director of the Center ofAutomation, which became a National Engineeringand Technology Research Center and a State Key Laboratory.He is a member of Chinese Academy of Engineering, IFACFellow and IEEE Fellow.He has served as director of Department of Information Science of National Natural Science Foundation of China from 2010 to 2018.

His current researchinterests include modeling, control, optimization and integrated automation of complex industrial processes. He has published 240 peer reviewed international journal papers.His paper titled Hybrid intelligent control for optimal operation of shaft furnace roasting process was selected as one of three best papers for the Control Engineering Practice Paper Prize for 2011-2013.He has developed control technologies with applications tovarious industrial processes.For his contributions, he has won 5 prestigious awards of National Natural Science,National Science and Technology Progress and National Technological Innovation, the 2007 Industry Award for Excellence in Transitional Control Research fromIEEE Multiple-conference on Systems and Control, and the 2017 Wook Hyun Kwon Education Award from Asian Control Association.



TitleReliablyAccurate State Estimation for Connected and Autonomous                        Highway Vehicles

Speaker: Professor Jay Farrell

Department of Electrical and Computer Engineering

University of California, Riverside, USA


Abstract: Accurate and reliable awareness of world interactions is a key requirement for effective commercial deployment of autonomous and connected vehicles. Awareness arises from onboard sensors and ubiquitous communication between vehicles and infrastructure. Vehicle coordination and safety necessitate reliable “where-in-lane” knowledge of vehicle state (sub-meter accuracy or better). This presentation will address sensor fusion for high-bandwidth vehicle state estimation with a focus on high accuracy and reliability.

Advances is sensing and computation have dramatically altered the focus of related research. For example, computer vision and Global Navigation Satellite Systems each separately provide far more measurements than are necessary for observability. Such environments are signal-rich. The large number of measurements provides both opportunities (e.g., high accuracy) and challenges (e.g., large numbers of outliers). Standard state estimation approaches that decide irrevocably at each time which measurements are valid (e.g. EKF)arenot sufficiently reliable at removing the effects of spurious measurements. When that decision is wrong, either measurement information is lost or the state and covariance estimates become corrupted, rendering all subsequent decisions suspect. Either situation can result in divergence of the state estimate, with potentially tragic consequences.

This presentation will consider moving horizon nonlinear state estimation by a risk-averse performance-specified (RAPS) approach. Moving horizon methods extract the Bayesian optimal trajectory using all sensor data over a temporal window (e.g. SLAM and RHE). RAPS modifies the optimization problem to select the least risky set of measurements that satisfies a user-defined performance constraint. RAPS is able to evaluate, and reconsider, outlier assumptions for all measurements within the temporal window. The presentation will include experimental results. 

Biography:

Jay A. Farrell earned B.S. degrees in physics and electrical engineering from Iowa State University, and M.S. and Ph.D. degrees in electrical engineering from the University of Notre Dame. While in the Autonomous Vehicles Group at Draper Lab, he received the Engineering Vice President's Best Technical Publication Award in 1990, and Recognition Awards for Outstanding Performance and Achievement in 1991 and 1993. He is a Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside. He has served the IEEE Control Systems Society (CSS) as President in 2014 and the American Automatic Control Council (AACC) as President in 2020-2021. He was named a GNSS Leader to Watch for 2009-2010 by GPS World Magazine in 2009 and a winner of the Connected Vehicle Technology Challenge by the U.S. Department of Transportation`s (DOT`s) Research and Innovative Technology Administration in 2011. He is author of over 250 technical publications and three books; a Distinguished Member of IEEE CSS; and, a Fellow of AAAS, IEEE, and IFAC.




TitleLearning-Based Control: A New Direction in Control Theory

Speaker: Professor Zhongping Jiang

New York University, USA




Abstract: This talk presents a new framework for learning-based control synthesis of continuous-time dynamical systems with unknown dynamics.  The new design paradigm proposed here is fundamentally different from traditional control theory. In the classical paradigm, controllers are often designed for a given class of dynamical control systems; it is a model-based design. Under the learning-based control framework, controllers are learned online from real-time input-output data collected along the trajectories of the control system in question. An entanglement of techniques from reinforcement learning and model-based control theory is advocated to find a sequence of suboptimal controllers that converge to the optimal solution as learning steps increase. On the one hand, this learning-based design approach attempts to overcome the well-known “curse of dimensionality” and the “curse of modeling” associated with Bellman's Dynamic Programming. On the other hand, rigorous stability and robustness analysis can be derived for the closed-loop system with real-time learning-based controllers. The effectiveness of the proposed learning-based control framework is demonstrated via its applications to connected and autonomous vehicles and human motor control.

Biography:

Zhong-Ping JIANG received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly.

Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written five books and is author/co-author of over 450 peer-reviewed journal and conference papers.

Dr. Jiang has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, a Fellow of the IFAC, a Fellow of the CAA and is among the Clarivate Analytics Highly Cited Researchers.