Farhad Bayat | fault-tolerant control | Best Researcher Award

Posted on by ScienceFather

Dr. Farhad Bayat | fault-tolerant control | Best Researcher Award

University professor, university of Zanjan, Iran

Farhad Bayat is a renowned Iranian researcher and academician in the field of control systems and electrical engineering. He has made significant contributions to the development of control systems, particularly in the areas of model predictive control, nonlinear control systems, and renewable energy systems. His work has been widely published in top-tier journals and conferences.

Profile

scopus

Education 🎓

Farhad Bayat received his B.S. in Electrical Engineering from Zanjan University (1999-2003). He then pursued his MSc in Electrical Engineering at Iran University of Science & Technology (2003-2006). Bayat completed his Ph.D. in Electronic Engineering at Iran University of Science & Technology (2006-2011). His academic excellence earned him top ranks in his undergraduate and graduate studies.

Experience 🧪

Farhad Bayat has accumulated extensive research experience in various institutions. He worked at Iran Tele Communication Research Center and Iran University of Science & Technology (2005-2006). Bayat was involved in designing a hybrid simulator for hardware-in-the-loop simulations of satellite attitude determination and control subsystems. He has also contributed to projects on wind turbine control, automotive control systems, and aerospace systems.

Awards & Honors🏆

Farhad Bayat has received numerous awards and honors for his outstanding contributions to science and engineering. Some notable recognitions include being selected as one of the top 2% of scientists in the world in 2024, assessed by Stanford University. He also received the Award for the best researcher in the field of engineering in Zanjan province in 2020.

Research Focus 🔍

Farhad Bayat’s research focuses on control systems, particularly in model predictive control, nonlinear control systems, and renewable energy systems. His work explores the development of novel control strategies for complex systems, including wind turbines, automotive systems, and aerospace systems. Bayat’s research aims to improve the efficiency, stability, and reliability of these systems.

Publications📚

1. 📊 An MPC-based fault tolerant control of wind turbines in the presence of simultaneous sensor and actuator faults.
2. 🚀 Robust non-aggressive three-axis attitude control of spacecraft: Dynamic sliding mode approach.
3. 💻 Optimal congestion management in network routers subject to constraints, disturbances, and noise using the model predictive control approach.
4. 🤖 LMI-based Luenberger observer design for uncertain nonlinear systems with external disturbances and time-delays.
5. 🦾 A nonsingular terminal sliding algorithm for swing and stance control of a prosthetic leg robot.
6. 💡 Sample-data output-feedback parameter variable control of glucose for type 1 diabetes mellitus patients.
7. 💻 Sampled-Data Linear Parameter Variable Approach for Voltage Regulation of DC–DC Buck Converter.
8. 🤖 Barrier function-based adaptive nonsingular terminal sliding mode control technique for a class of disturbed nonlinear systems.
9. 🚀 Nonsingular terminal sliding mode control for micro-electromechanical gyroscope based on disturbance observer: Linear matrix inequality approach.
10. 🚗 Robust Performance Improvement of Lateral Motion in Four-Wheel Independent-Drive Electric Vehicle.
11. 📊 Model Predictive Control: Theory and Applications.
12. 📚 Theory of automatic control systems.
13. 🤖 Toolbox developed for MATLAB: MPC3S: MPC in 3 Steps Toolbox.
14. 📊 Model Predictive Sliding Control for Finite-Time Three-axis Spacecraft Attitude Tracking.
15. 🤖 Composite Nonlinear Feedback Design for Discrete-Time Switching Systems with Disturbances and Input Saturation.
16. 📊 Constrained Linear Parameter-Varying Control using Approximate Multi-Parametric Programming.
17. 📊 Optimal Observer Designing for eMPC.
18. 📊 Two-Stage Observer Based Offset-Free MPC.
19. 📊 On the Performance of Observer-Based Explicit Model Predictive Control.
20. 📊 Design of explicit model predictive control for constrained linear systems with disturbances.
21. 🤖 Flexible Piecewise Function Evaluation Methods Based on Truncated Binary Search Trees and Lattice Representation in Explicit MPC.
22. 📊 Managing time-storage complexity in point location problem: Application to Explicit Model Predictive Control.
23. 📊 Comments on Analytical expression of explicit MPC solution via lattice piecewise-affine function.
24. 📊 Using hash tables to manage time-storage complexity in point location problem: Application to Explicit MPC.
25. 🚀 Global Stabilizing Magnetic Controller for Satellite Attitude Detumbling and Hardware In the Loop Implementation.
26. 📊 Optimal Control of Linear Constrained Systems: Multi-Parametric Programming Approach.
27. 🚀 Artificial Missile Guidance Using On-Line Adaptive Neural Networks.
28. 🤖 An Efficient Algorithm for Clustering of Convex and Non-Convex Data.
29. 🚀 Attitude Control Of Spinning Satellite Subject To Actuators

Conclusion

Dr. Farhad Bayat’s impressive academic background, research excellence, interdisciplinary approach, and leadership skills make him an outstanding candidate for the Best Researcher Award. While there are areas for improvement, his strengths and achievements demonstrate his potential to make a significant impact in his field.

Fangfang Zhang | Control sicence | Best Researcher Award

Posted on by ScienceFather

Prof. Fangfang Zhang | Control sicence | Best Researcher Award

Professor, Qilu University of Technology (Shandong Academy of Sciences), China

Dr. Zhang Fangfang is an accomplished researcher in control theory, artificial intelligence, and nonlinear systems. She serves as an Associate Professor at the School of Information and Automation, Qilu University of Technology, and has held multiple visiting scholar positions at prestigious institutions, including the Chinese Academy of Sciences and City University of Hong Kong. With a strong background in system optimization, atmospheric turbulence analysis, and chaotic secure communication, she has led numerous research projects funded by the National Natural Science Foundation of China and Shandong Province. Dr. Zhang has published over 130 SCI/EI papers, including highly cited works, and has secured 20 invention patents, including two US patents. Her contributions to education and research have been recognized with multiple prestigious awards. She is an active reviewer for international journals and serves in various academic committees, further advancing research in automation and artificial intelligence.

Profile

scopus

Educational Experience 

Dr. Zhang Fangfang holds a Doctoral Degree in Control Theory and Control Engineering from Shandong University (2010–2014). She earned her Master’s Degree in the same discipline from Beijing University of Technology (2003–2006) and a Bachelor’s Degree in Automation from Northeast Petroleum University (1999–2003). Her academic journey has provided her with a strong foundation in automation, control engineering, and artificial intelligence. Throughout her education, she developed expertise in nonlinear systems, system optimization, and chaotic dynamics, setting the stage for her groundbreaking research in fault diagnosis, atmospheric turbulence, and secure communication. Her doctoral research focused on advanced control methodologies and their applications in complex engineering systems, contributing significantly to the field. With a multidisciplinary approach, she integrates artificial intelligence techniques into automation and control systems, paving the way for innovations in industrial and academic research.

Work Experience 

Dr. Zhang Fangfang is an Associate Professor at Qilu University of Technology, where she has been teaching since 2014. She was promoted from Lecturer (2014–2017) to Associate Professor (2018–present) due to her outstanding contributions to research and academia. She has also held several prestigious visiting scholar positions, including at the Aerospace Information Research Institute, Chinese Academy of Sciences (2023–2024), the Research Center for Chaos and Complex Networks, City University of Hong Kong (2019–2020), and the Department of Computer Science, University of Otago, New Zealand (2018–2019). Additionally, she completed a postdoctoral research fellowship in Systems Engineering at Shandong University (2018–2022). Her professional experience is deeply rooted in interdisciplinary research, focusing on control engineering, artificial intelligence, and nonlinear systems. She has successfully led and participated in major research projects, establishing herself as a key figure in the advancement of automation, intelligent control, and secure communication.

Awards and Honors 

Dr. Zhang Fangfang has received multiple prestigious awards for her outstanding contributions to science and education. She won the First Prize of the Natural Science Award (Zhang Siying Award) from the Shandong Institute of Automation and the First Prize of the Science and Technology Award from Shandong Machinery Industry. Her dedication to higher education earned her the First Prize of the Teaching Achievement Award in Robotics and Artificial Intelligence Education at the 23rd session. She also secured the Provincial Teaching Achievement Award (First Prize) in Shandong Province. Her research excellence was further recognized with the Second Prize of the Science and Technology Award of Shandong Higher Education Institutions and multiple other teaching and research accolades. Dr. Zhang is an active member of professional organizations, including the Chinese Association of Automation and the Chinese Association for Artificial Intelligence, where she contributes to shaping the future of intelligent automation and control engineering.

Research Focus 

Dr. Zhang Fangfang’s research spans control engineering, artificial intelligence, nonlinear systems, and secure communication. She specializes in fault diagnosis, system optimization, atmospheric turbulence analysis, and chaotic dynamics, developing innovative solutions for industrial and scientific applications. Her work in chaotic secure communication enhances data security, while her research on nonlinear system behaviors contributes to improved automation control methods. Dr. Zhang has extensively studied detection and control systems, integrating artificial intelligence to improve their efficiency and accuracy. She also investigates atmospheric turbulence and its chaotic characteristics, providing new insights into complex environmental and industrial processes. Her research is highly interdisciplinary, bridging automation, AI, and nonlinear dynamics. With a strong publication record in top-tier journals, she continues to push the boundaries of innovation in control systems, intelligent automation, and cybersecurity, making significant contributions to both academia and industry.

Publications

“Analysis of Chaotic Secure Communication Based on Nonlinear System Theory”
📄 “Detection and Control of Atmospheric Turbulence Using AI-Based Optimization Models”
📄 “Fault Diagnosis and Intelligent Control in Industrial Automation Systems”
📄 “Nonlinear Dynamics in Complex Networks: A Chaotic Perspective”
📄 “System Optimization for Robust Control in Smart Manufacturing”
📄 “Artificial Intelligence-Based Strategies for Enhancing Secure Communication”
📄 “New Approaches in Machine Learning for Nonlinear System Identification”
📄 “Application of Deep Learning in Predictive Maintenance of Automation Systems”
📄 “Advanced Methods for Analyzing Chaotic Characteristics in Environmental Systems”
📄 “Integration of AI and Control Theory for Next-Generation Robotics”

Conclusion

Zhang Fangfang is an exceptional candidate for the Best Researcher Award, given her outstanding contributions to control science, automation, and artificial intelligence. Her extensive publication record, patents, and leadership in national projects reflect her impact on the field. To further solidify her global recognition, enhancing international collaborations and real-world industry applications could be beneficial. Nevertheless, her achievements in research, innovation, and education make her a highly deserving recipient of the award.