Speakers

Mr. Ahmed RUBAAI, Fellow IEEE

Professor and Chairperson
Electrical Engineering and Computer Science
Howard University, Washington, DC, USA

Biography

AHMED RUBAAI received the M.S.E.E degree from Case Western Reserve University, Cleveland, OH, and the Dr. Eng. degree from Cleveland State University, Cleveland, OH, in 1983 and 1988, respectively. In 1988, he joined Howard University, Washington, DC, as a faculty member, where he is presently a Professor and Chairperson of the Electrical Engineering and Computer Science Department. Dr. Rubaai has been named an IEEE Fellow in 2015. He has made significant contributions to the development and control of electric motor drives for industrial system applications in a variety of roles including scientist, research engineer, university professor, and as IEEE volunteer and leader. Most of these contributions are heavily oriented towards industrial applications that IEEE serves. Of importance is his development of control technologies by way of intelligence; laying the technological foundations for the production versions of high-performance drives used in an expansive array of industrial, commercial, and transportation applications today. His work covers a broad range of manufacturing and product applications and exemplifies his ability to bridge between academic research and the application to industrial applications. The bridges that Dr. Rubaai has built between industry and academia represent a uniquely valuable contribution that can be matched by very few others in the academic world today. Dr. Rubaai is the Founder and Lead Developer of Motion Control and Drives Laboratory that provides engineering students with valuable hands-on and “real-world” experiences.” In recognition of his scholarly work and dedication to the improvement of engineering education, his work is recognized by the larger community of engineering educators, as verified by his receipt of the 2011 ASEE Robert G. Quinn Award and the Distinguished Educator Award of the Middle-Atlantic Section of the American Society for Engineering Education. This recognition is a clear demonstration and confirmation of his peers’ high regard for his contributions to engineering education.

DSP-Based Hybrid H∞ Adaptive Fuzzy Tracking Control Structure for High Performance Drives: A Real-Time Implementation

Abstract

A hybrid H∞ adaptive fuzzy control structure is proposed for trajectory tracking control of a brushless drive system (BSDS).  The control structure consists of three building blocks: 1) a fuzzy logic controller, 2) an adaptive law that modifies the parameters of the fuzzy controller based on a Lyapunov synthesis approach, and 3) an H∞ tracking system. The H∞ tracking controller can take care of the robust stabilization and disturbance rejection problems, while, fuzzy logic controller is used as principle components of the adaptive fuzzy controller. It is outfitted with an adaptive control law based on a Lyapunov synthesis approach to compensate for system uncertainty and random changes in the external load acting on the drive system.  The ability of the controller to achieve the tracking process with a high degree of accuracy, even in the presence of random disturbance, was also demonstrated. This sudden (random) change in the load was performed for several reference tracks, and very promising results were observed.  The proposed control structure is experimentally verified on a state-of-the-art dSPACE DS1104 digital signal processor (DSP)-based data acquisition and control system in a laboratory 1-hp brushless drive system. Experimental results are provided to verify the effectiveness of the proposed controller. Considerable improvement in performance generated by the hybrid controller compared with the traditional H∞ controller.

Computational Intelligence Teaching-Based FNN PI/PD-Like Fuzzy Control for Industrial Drives: Hardware/Software Implementation 

Abstract

 A fuzzy-neural-network Promotional-Integral (PI)-and Promotional-Derivative (PD)-type control design is offered to replace the industrial Promotional-Integral-Derivative (PID) controller, adds a self-learning capability to the initial fuzzy design for operational adaptively, and implements the solution on real hardware using an industrial test-bed system. First, the proposed solution is to use fuzzy logic-based decision structures to mimic the PI and PD elements of a PID controller in parallel. The fuzzy decision engines seek to improve response by executing custom actions per the combinations of fuzzy sets of the input parameters to the logic. Operational knowledge of the physical meaning of the fuzzy input set combinations and the necessary control response form the basis for the fuzzy rules design. Second, a fuzzy-neural-network (FNN) structure that replaces the fuzzy logic in the control design and allows for the capability for self-tuning of the weights and memberships of the input parameters is introduced. This leads to selection of a learning algorithm for training the networks. The design implements the novel use of the extended Kalman filter (EKF) to train FNN structures as part of the PI-/PD-like fuzzy design. The benefits of the proposed control providing access to the fuzzy rules online and the proper execution of the updates are improved control law maintenance operations. A test bench enables design implementation in the laboratory on hardware using a dSPACE DS1104 DSP and MATLAB/Simulink environment. Experimental testing results show that the proposed controller robustly responds to a wide range of operating conditions in real time. The drive system over time is subject to experience degradation of mechanical parts or even electrical characteristics, and the ability to automatically adapt the control laws to these changes is a feature that a non-adaptive fuzzy controller does not have.

Prof. Amer Ragab ZEREK

Professor of communication engineering
University of Zawia, Libya
IEEE senior member

Biography

Amer Ragab ZEREK (IEEE senior member) is a Professor of communication engineering 2022, Libya. He obtained his undergraduate degree in communication engineering (1982) from Al-Fateh University (Tripoli University), Tripoli and in 1991 he obtained an MSc in Electronic Engineering at University of Wales Cardiff, U.K. Prof Zerek obtained PhD degree in Electronic Engineering at Wales University Cardiff., U.K., 1996. He has more than 160 published papers. He has written a book titled “fundamental of communication engineering”. He is steering and scientific committee member in several conferences at Tunisia, Algeria, India, France, Morocco, Libya, etc. As well as Prof Zerek is a reviewer and technical program chair in some conferences. He is steering committee chair in both the 1st and 2nd International Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA’2021 and MISTA 2022). Prof Zerek is a Libyan ambassador in the 18th & 19th IEEE International Multi-Conference on System, Signals and Devices “SSD21& 2022”.

Industrial Software as Teaching Tool for Communication Systems

Abstract

This lecture presents examples of simulated experiments using MATLAB Simulink, as illustration to help students grasp the fundamentals of communication systems. The simulated experiments deal with digital and analog modulations (AM, SSD-SC, DSB-SC, FM, PM, PWM, PPM, PAM, SDM, PCM, …etc) to demonstrate how signals are transformed by the modulation process in both time and frequency domains. In addition, the simulated experiments deal with multi-level (M-ary), PSK, DPSK and PAM, modulation for M=4, 8, 16, 32, 64, 128 and 256, so that the proposed systems implement multi antenna diversity of 2×3 (where the transmitter antennas is two and the receiver antennas is three). Both AWGN, Rayleigh and Rician channels are considered using Multi-Input-Multi-Output (MIMO) system to achieve the Bit Error Rate (BER) performance and constellation diagrams. Students can easily learn how to experiment with this software at their own time and pace. This helps visualize the communication systems principles and any other physical system.