Doctor of Engineering (D.Eng.)
Electrical Engineering
Cleveland State University
1988
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Faculty
Faculty
Ahmed Rubaai
Professor
Department/Office
- Department of Electrical Engineering and Computer Science, CEA
School/College
- College of Engineering and Architecture (CEA)
Education & Expertise
Education
Master of Science (M.S.)
Electrical Engineering
Case Western Reserve University
1983
Bachelor of Science (B.S.)
Electrical Engineering
El-Fateh University
1978
Research
Research
Funding
Co-Principal Investigator: Center of Excellence in Artificial Intelligence & Machine Learning, DOD, $7.50 million, Sept 16, 2020-Oct 15, 2024.
Principal Investigator: Apple Innovation: Expanding Silicon and Hardware Engineering curriculum in partnership with Apple’s experts, Apple, $1.2 million, June 17, 2021-July 15, 2024.
Co-Principal Investigator:, EAGER: SaTC-EDU: Discovery, Analysis, Research and Exploration Based Experiential Learning Platform Integrating Artificial Intelligence and Cybersecurity, NSF, $300,000, August 1, 2020-July 31, 2022.
Co-Principal Investigator: develop and implement an onboard intelligent model-based hybrid electric propulsion control system, NASA, $800K, May 15, 2021- June 15, 2024.
Principal Investigator: Development of new Silicon Lab, Microsoft, $100,000, July 2012-December 31, 2023.
Principal Investigator: Development of new Silicon Lab, Autodesk, $100,000, July 2012-December 31, 2023.
Principal Investigator: Bridging the Gap Between Academia and the Real World for Underrepresented Students: Cisco Tech Fellows Program,” Cisco, $585,000, January 7, 2021-October 31, 2024.
Principal Investigator: Improving Undergraduate Student Performance in Electrical Engineering, Computer Engineering and Computer Science Programs at Howard University, Intel, $600,000, July 1, 2017-June 30, 2022.
Principal Investigator: Enhancing Pipeline of Talented Students from Underrepresented Background with Research Experiences in Emerging Areas of Technology, Intel, $230,000, June 15, 2021-July 1, 2023.
Principal Investigator: Cloud Computing Research and Curriculum Development, Intel Labs, $100,000, January 18, 2022-December 31, 2022.
Principal Investigator: Unmanned Aerial Vehicle Testbed for Cyber Security Attack: Analysis, Modeling and Mitigation, Microsoft, $134,000.00, June 1, 2021- July 31, 2023.
Principal Investigator: Preparing the Next Generation Workforce in chip design, Micron Foundation, $100,000, December 31, 2022, January 1, 2023.
Principal Investigator: Experience in Teamwork, and Industrial-Grade Design Validation: Autodesk Tech Fellow Program, Autodesk, $184,500, July 1, 2020-June 30, 2022.
Principal Investigator: Microsoft Tech Fellows Program, Microsoft, $127,563.00 May 1, 2018- Dec 30, 2021.
Principal Investigator: “Practical and Research Experiences in Undergraduate Education,” Aerospace, $75,000, Sept 1, 2021- July 31, 2023.
Principal Investigator: Enhancing the Quality of Undergraduate STEM Education, Precise Foundation, $32,000, May 31, 2021-April 30, 2022.
Principal Investigator: Bechtel $25,000: Bechtel Corporation Fund, April 2024.
Principal Investigator: AT&T, $25,000: AT&T Corporation Fund, July 2024.
Accomplishments
Accomplishments
Named 2015 IEEE Fellow, for “Contributions to the Development of High-Performance Controls for Electric Motor Drives.”
Recipient, 2011 American Society for Engineering Education (ASEE) Robert G. Quinn Award for excellence in engineering experimentation and laboratory instruction
Publications and Presentations
Publications and Presentations
Book Chapters
Chapter 37: Fuzzy Logic Applications in Electrical Drives and Power Electronics in Power Electronics Handbook, 3rd Edition (pp. 1115-1137)
This chapter focuses on fuzzy logic applications in electrical drives and power electronics. The application of fuzzy reasoning to improve the proportional-integral-derivative (PID) controller is evident in the research community today to build state-of-the-art control systems.
Chapter 35: Fuzzy Logic Applications in Electrical Drives in Power Electronics Handbook, 2nd Edition (pp. 99-1013)
Power electronics, which is a rapidly growing area in terms of research and applications, uses modern electronics technology to convert electric power from one form to another, such as ac-dc, dc-dc, dc-ac, and ac-ac with a variable output magnitude and frequency. Power electronics has many applications in our every day life such as air-conditioners, electric cars, sub-way trains, motor drives, renewable energy sources and power supplies for computers. This book covers all aspects of switching devices, converter circuit topologies, control techniques, analytical methods and some examples of their applications.
Chapter 18: Fuzzy Logic Control for Power Networks: A Multilayer Fuzzy Controller in Advanced Fuzzy Logic Technologies in Industrial Application Handbook (pp. 261-277)
The ability of fuzzy systems to provide shades of gray between "on or off" and "yes or no" is ideally suited to many of today’s complex industrial control systems. The static fuzzy systems usually discussed in this context fail to take account of inputs outside a pre-set range and their off-line nature makes tuning complicated.
Advanced Fuzzy Logic Technologies in Industrial Applications addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs.
Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things Networks
This article investigates the problem of estimating complex-valued Gaussian signals in an industrial Internet of Things (IIoT) environment, where the channel fading is temporally correlated and modeled by a finite state Markov process. To address the non-trivial problem of estimating channel fading states and signals simultaneously, we propose two deep learning (DL)-aided minimum mean square error (MMSE) estimation schemes.
GAN-based Channel Estimation for IRS-aided Communication Systems
GAN-based Channel Estimation for IRS-aided Communication Systems
This paper proposes a generative adversarial network (GAN) based channel estimation scheme for intelligent reflecting surface (IRS)-aided single-input multiple-output (SIMO) communication systems. The proposed novel GAN-based deep learning technique is efficient to estimate channels in IRS-aided wireless communication systems with high accuracy. The generator of GAN can reproduce data whose distributions are similar to the actual underlying channel. Consequently, the proposed approach does not require the statistical distribution of the underlying channel to be known in advance. Simulation results prove that the proposed GAN-based channel estimation approach outperforms the conventional least square estimation (LSE) approach significantly in terms of estimation accuracy as well as provides better performance than a fully connected deep neural network (DNN) and convolutional neural network (CNN)-based methods.
Testing the Frame-Angle-Based Direct Torque Control for 3φ Induction Motor Drives
Testing the Frame-Angle-Based Direct Torque Control for 3φ Induction Motor Drives
This article presents the performance evaluation of the frame-angle-based (FAB) direct torque controller (DTC) for 3φ induction motor drives (IMDs), which are fed by 3 six-pulse wavelet-modulated (WM) dc-ac power electronic converters (PECs). The tested DTC is designed to adjust the de- and qe-axis components of the voltages (veds and veqs) applied to a 3 IMD. The adjustments in veds and veqs are created in response to changes in the load torque, command torque, drive speed, and/or system parameters. Desired adjustments in veds and veqs are set by regulating the angle ϑ of the frame created by veds and veqs. This frame produces reference modulating signals that are required to generate WM switching pulses for operating the 3 dc-ac PEC in the IMD. The complete IMD system, incorporating the FAB-DTC, is implemented for a 10-hp IMD system for performance evaluation. Tests of the FAB-DTC for the 10-hp IMD are conducted for various changes in the load torque, command torque, drive speed, and system parameters. Simulation and experimental test results demonstrate fast, accurate, reliable, and dynamic responses with minor sensitivity to variations in system parameters.
The Performance of the Frame-Angle-Based Direct Torque Controller for PMSM Drives at Low and Very Low Speeds
This paper extends the performance testing of the frame-angle-based (FAB) direct torque controller (DTC) for permanent magnet synchronous motor (PMSM) drives, which are fed by 3φ., 6-pulse wavelet modulated (WM) dc-ac power electronic converters (PECs). The performance testing of the FAB-DTC is extended to the low and very low speed operation of PMSM drives. The FAB-DTC regulates the d-q-axis components of PMSM stator voltages (vd and vq)., in response to variations in the load torque and/or drive speed. The adjustments in vd and vq are created by changing the angle ϑ of the frame spanned by vd and vq. In order to extended the FAB-DTC to low and very low speeds, the changes in ϑ are accompanied by changes in the maximum scale J. The values of J, vd., and vq are used to update or change the reference signals employed by the wavelet modulation to generate switching pulses to operate 3φ dc-ac PEC. The complete PMSM drive system incorporating the modified FAB-DTC is simulated for a 10-hp PMSM drive system. The performance of the FAB-DTC is investigated for different changes in the command torque for various low and very low speeds. Performance results demonstrate stable, fast, dynamic, and accurate responses, which have minor sensitivity to variations in load torque and/or drive speed.
The Development and Performance Evaluation of a Frame-Angle-Based Direct Torque Controller for PMSM Drives
This paper presents the development and performance evaluation of a direct torque controller (DTC) for permanent-magnet synchronous motor (PMSM) drives fed by a 3ϕ 6-pulse wavelet-modulated dcac power electronic converters. The developed DTC is designed to adjust the d−q-axis components of stator voltages (vd and vq) of a PMSM in response to changes in the load torque or drive speed. The desired adjustments in vd and vq are achieved by changing the angle ϑ of a frame created by vd and vq. This frame is responsible for updating or changing the reference modulating signals that are required by the wavelet modulation technique to generate switching pulses. The complete PMSM drive system incorporating the developed DTC is implemented for a 10 -hp PMSM drive system. The performance of the frame-angle-based DTC is investigated for different changes in the command torque and drive speed. Simulation and experimental test results demonstrate stable, fast, and accurate responses that are complimented by negligible sensitivity to variations in the system parameters.
Computer Aided Instruction of Power Transformer Design in the Undergraduate Power Engineering Class
Computer Aided Instruction of Power Transformer Design in the Undergraduate Power Engineering Class
This paper describes a single-phase transformer design suitable for classroom use. The scope of this design is limited to the specification for the core and coils of the transformer. Both shell and core configured transformers are designed in this paper. A computer program is developed for the purpose of illustrating the design procedure and demonstrating how it works. The objective is to meet all performance requirements at minimum cost.
