Prof. Chai Tianyou

Northeastern University, China

Biography: Tianyou Chai received the Ph.D. degree in control theory and engineering in 1985 from Northeastern University, Shenyang, China, where he became a Professor in 1988. He is the founder and Director of the Center of Automation, which became a National Engineering and Technology Research Center and a State Key Laboratory. He is a member of Chinese Academy of Engineering, IFAC Fellow and IEEE Fellow, director of Department of Information Science of National Natural Science Foundation of China.
His current research interests include modeling, control, optimization and integrated automation of complex industrial processes.
He has published 180 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 to various industrial processes. For his contributions, he has won 4 prestigious awards of National Science and Technology Progress and National Technological Innovation, the 2007 Industry Award for Excellence in Transitional Control Research from IEEE Multiple-conference on Systems and Control.

Title of Speech: CPS driven optimal control system for energy-intensive equipment

Abstract: China 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 and complex 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 driven optimal control system.
This talk presents the syntheses and implementation of a CPS driven optimal control system for energy-intensive equipment under the framework of CPS. The proposed CPS driven optimal control system consists of three main functions: (i) setpoint control; (ii) tracking control; and (iii) self-optimized tuning. 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 driven optimal control 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 driven optimal control system for energy-intensive equipment developed by our team is introduced. The results of simulation experiments show the effectiveness of the proposed method that integrates the setpoint control, tracking control and self-optimized tuning in the framework of CPS.
The industrial application of the proposed CPS driven optimal control 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 the CPS driven optimal control system is outlined.

Prof. Abderazek Ben Abdallah

The University of Aizu, Japan

Biography: Aderazek Ben Abdallah is a full Professor of Computer Science and Engineering and the Head of the Division of Computer Engineering, the University of Aizu since April 2014. He has been a faculty member at the Division of Computer Engineering, the University of Aizu since 2007. Before joining the University of Aizu, he was a research associate at the Graduate School of Information Systems, the University of Electro-Communications at Tokyo from 2002 to 2007. Dr. Ben Abdallah received his Ph.D. degree in Computer Engineering from the University of Electro-Communications at Tokyo in 2002. His research interests cover a broad spectrum of areas, including adaptive/self-organizing systems, networks-on-chip, power & reliability-aware architectures, neuro-inspired information processing systems, and processor architecture. He has authored three books, published more than 150 journal articles and conference papers in these areas and given invited talks as well as courses at several universities, including Hong Kong University of Science and Technology (HKUST) and Huazhong University of Science and Technology (HUST). He has been a PI or CoPI of several projects for developing next generation high-performance reliable computing systems for applications in general purpose and pervasive computing.
He is a senior member of IEEE, and ACM and a member of IEICE. He was awarded a Presidential prize for scientific research and technology in 2010, and several best-paper awards (BWCCA2010, FAN2009, PDCAT2007). He served on the chair, editorial, and review boards of several journals and conferences including, steering chair of the IEEE MCSoC Symposium Series. He has been also involved in co-organizing many symposia, and conferences as well as guest editor of special issues in journals, such as IEEE Transactions on Emerging Topics in Computing. 

Title of Speech: Neuro-inspired Computing Systems and Applications

Abstract: The biological brain implements parallel computations using a complex structure that is different from the conventional von Neumann or load/store computing style. Our brain is a low-power, fault-tolerant, and high-performance machine! Hardware implementations of artificial neural (AI) networks are efficient and effective methods to provide cognitive functions on a chip. This talk first presents an overview of the low-level neuron modelling, on-chip learning and the upper level AI models applications. The second part of the talk focuses on DNN (deep neural network) hardware implementation with specialized adaptive hardware components and on-line learning capability on a manycore adaptive system-on-chip (SoC). A case study on low-power and high-performance Neuro-inpired SoC implementiaon based on convolution neural network and adaptive learning is given. The adaptive neuro-inpisred SoC is targeted for a new class of applications ranging from autonomous IoT devices, intelligenet robots, and deep learning to high-performance computing co-/processing.

Prof. Chiang-Ju Chien

Huafan University, Taiwan

Biography: Dr. Chien received the Ph.D degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in 1992. Since 1993, he was with the Department of Electronic Engineering, Huafan University, Taipei, Taiwan, where he is currently a professor. In the past 15 years, Dr. Chien has served as the Chair of Department of Electronic Engineering, Dean of College of Engineering and Management, Convener of Internal Control and Audit Committee, Acting President and currently the Secretary General at Huafan University. He has published more than 130 peer reviewed international journal and conference papers in the area of control theory and its applications. Moreover, he is appointed as a reviewer for many outstanding international journals such as Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics, and so on. He also served as a final review commissioner for the research projects of Taiwan Ministry of Science and Technology and the member of 2017 institutional accreditation for Higher Education Evaluation & Accreditation Council of Taiwan.
Dr, Chien received the Research Award (Category A) from National Science Council, Taiwan in 1995, 1997, 1999 and the best paper awards at The 17th National Conference on Fuzzy Theory and Its Applications 2009, and International Automatic Control Conference 2016. His work was awarded as one of the top research articles published in 2014 and 2015 at International Journal of Fuzzy Systems. His current research interests are mainly in iterative learning control, adaptive control, fuzzy-neuro systems, and control circuit design.

Title of Speech: Neural Network Based Iterative Learning Control for Nonlinear Systems

Abstract: In this talk, an iterative learning control (ILC) algorithm for nonlinear systems by using neural network design is presented. Iterative learning control is one of the most effective control strategies for systems which are asked to repeat the control tasks over and over again during a finite interval. In this study, we aim to propose a neural network based iterative learning controller so that only input/output data are required for the controller design. A new approach using two neural networks is employed. The first neural network called neural controller (NC) is used as the main iterative learning controller for the closed loop stability. An adaptive law is developed to update the neural weights during the control iterations in order to guarantee the error convergence. As the information of system sensitivity function is required to design the adaptive law, a second neural network called neural identifier (NI) is used as an estimator to provide an estimated sensitivity function. The weights of neural identifier will be tuned after the current trial and a good estimated sensitivity function can be provided for next iteration. The adaptive laws for the weights of both neural controller and neural identifier as well as the analysis of closed loop learning performance are determined via a Lyapunov like analysis. A comparison between this new design and traditional PID-type iterative learning control will also be discussed. Finally, some numerical simulations and experimental results are shown to demonstrate the effectiveness of the proposed scheme.