Amin Najafi | Robotics and Automation | Best Researcher Award

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Mr. Amin Najafi | Robotics and Automation | Best Researcher Award

PhD candidate at University of Zanjan, Iran.

Amin Najafi is a researcher specializing in advanced fault-tolerant control, robotics, and intelligent transportation systems. His expertise lies in designing resilient control algorithms for UAVs, MAGLEV trains, and autonomous guidance systems. Through a strong portfolio of high-quality publications, Najafi has contributed significantly to enhancing the stability, safety, and performance of robotic systems operating under uncertain and fault-prone conditions. His work in adaptive barrier sliding mode control and finite-time stabilization has been widely recognized for bridging theoretical advancements with practical applications. Najafi’s research has appeared in leading journals, including IEEE Transactions on Transportation Electrification, Mathematics, ISA Transactions, and the Journal of Vibration and Control. Beyond research, he actively contributes to the scientific community through peer-review engagements across prestigious journals. His growing influence demonstrates his commitment to advancing robust, intelligent, and reliable autonomous systems, making him a promising candidate for recognition in robotics and automation research.

Professional Profile

Google Scholar | Scopus | ORCID

Education

Amin Najafi’s academic training has been grounded in control engineering, robotics, and automation. His education equipped him with advanced knowledge in nonlinear control, adaptive systems, and fault-tolerant design, laying a strong foundation for tackling complex challenges in autonomous platforms. Building on this foundation, Najafi engaged deeply with theories of stability, guidance, and fault diagnosis while also exploring practical aspects of UAVs and intelligent transportation. His progression through academic programs allowed him to develop both analytical rigor and applied research capabilities. The interdisciplinary nature of his training helped him connect mathematics, control theory, and engineering applications, which is reflected in his publications that combine theoretical robustness with engineering relevance. Najafi’s educational journey reflects a balance of theory and practice, giving him the ability to produce impactful work that speaks to both the academic community and the broader engineering industry in robotics and automation.

Experience

Amin Najafi has developed his career around solving critical problems in robotics, automation, and transportation electrification. His research experience includes designing innovative fault-tolerant controllers for quadrotor UAVs, advancing resilient strategies for MAGLEV train systems, and contributing to aerospace and defense-related guidance systems. His international collaborations with researchers such as S. Mobayen, A. Fekih, and L. Fridman demonstrate his ability to work within diverse, high-caliber teams. Najafi has also built strong credentials as a peer reviewer, having reviewed more than 60 manuscripts for prestigious journals including IEEE Transactions on Transportation Electrification, IEEE Access, and the Asian Journal of Control. This dual role as an author and reviewer highlights both his subject matter expertise and his standing in the global robotics and control community. Through his experience, he has consistently contributed to advancing autonomous and fault-resilient systems, ensuring his research holds both academic and applied significance.

Research Focus

Najafi’s research is anchored in fault-tolerant control, nonlinear dynamics, and resilient robotics. His primary focus lies in developing adaptive barrier sliding mode controllers, finite-time stabilization strategies, and robust diagnosis methods for actuator faults. UAVs represent a central application in his portfolio, where he has addressed actuator reliability, real-time guidance, and performance optimization under uncertain conditions. Beyond UAVs, he has extended his contributions to MAGLEV trains and interceptor-target systems, demonstrating the versatility of his control strategies. His work is characterized by integrating theoretical rigor, such as linear matrix inequality approaches, with real-world engineering challenges, making his contributions impactful across multiple domains. The broader vision of his research is to enable safe, intelligent, and adaptive robotic systems capable of operating in dynamic and fault-prone environments. By combining control theory with automation and robotics, Najafi continues to advance the frontiers of resilient and intelligent autonomous technologies.

Publication Top Notes

Title: Adaptive Barrier Fast Terminal Sliding Mode Actuator Fault-Tolerant Control Approach for Quadrotor UAVs
Authors: A. Najafi, M.T. Vu, S. Mobayen, J.H. Asad, A. Fekih
Journal: Mathematics.
Citations: 51
Summary: Proposes an adaptive barrier fast terminal sliding mode controller for quadrotor UAVs. Ensures finite-time stability, fault tolerance, and resilience against actuator faults with validated simulations.

Title: Design of Linear Matrix Inequality-Based Adaptive Barrier Global Sliding Mode Fault-Tolerant Control for Uncertain Systems with Faulty Actuators
Authors: K. Naseri, M.T. Vu, S. Mobayen, A. Najafi, A. Fekih
Journal: Mathematics.
Citations: 22
Summary: Introduces an LMI-based adaptive barrier global sliding mode controller. Provides robust stability and effective fault management in uncertain nonlinear systems.

Title: Robust Adaptive Fault-Tolerant Control for MAGLEV Train Systems: A Non-Singular Finite-Time Approach
Authors: A. Najafi, S. Mobayen, S.H. Rouhani, Z. Mokhtare, A. Jalilvand, L. Fridman, et al.
Journal: IEEE Transactions on Transportation Electrification.
Citations: 3
Summary: Develops a finite-time robust adaptive controller for MAGLEV trains. Enhances fault tolerance, passenger safety, and system robustness under disturbances.

Title: Multiple Actuator Fault Diagnosis Based on Parity Space for Quadrotor System
Authors: A. Najafi, D. Bustan
Journal: Journal of Aeronautical Engineering (JOAE).
Citations: 2
Summary: Presents a parity-space-based approach to detect and isolate multiple actuator faults in quadrotors, ensuring reliable UAV performance.

Title: Design of Adaptive Barrier Function-Based Backstepping Finite-Time Guidance Control for Interceptor-Target Systems
Authors: Z. Mokhtare, M.A. Sepestanki, S. Mobayen, A. Najafi, W. Assawinchaichote, et al.
Journal: Journal of Vibration and Control.
Citations: –
Summary: Proposes a backstepping control method with adaptive barrier functions for interceptor-target systems. Guarantees finite-time convergence and robust guidance under uncertainties.

Conclusion

Amin Najafi demonstrates strong potential and achievement in fault-tolerant control systems for UAVs and transportation applications, with impactful publications, innovative methodologies, and active engagement in peer review. While there is scope for growth in terms of citation impact and broader collaborations, his research contributions are highly relevant to the advancement of resilient and intelligent autonomous systems. He can be considered a suitable and promising candidate for the Best Researcher Award, particularly within the subject category of Control Systems, UAVs, and Intelligent Transportation.

Chao Yang | Mechanical Engineering | Best Researcher Award

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Prof. Dr. Chao Yang | Mechanical Engineering | Best Researcher Award

Associate Professor, Jiaxing University, China.

Dr. Chao Yang, born in 1982, is currently a Lecturer in the School of Mechanical Engineering at Jiaxing University, China. He holds a Ph.D. in Mechanical Engineering from Zhejiang Sci-Tech University (2019), preceded by an M.E. in Engineering Mechanics from Dalian University of Technology (2009) and a B.E. in Process Equipment & Control Engineering from Zhengzhou University of Light Industry (2005). Since joining academia, Dr. Yang has consistently contributed to advancing the mechanics of parallel manipulators, focusing on kinematics, dynamics, stiffness analysis, and intelligent optimization algorithms. With over 20 peer-reviewed publications in reputable international journals and Awards, he has become a recognized figure in robot modeling and design. Dr. Yang also serves as a reviewer for journals like Mechanism and Machine Theory and Applied Mathematical Modelling, further reflecting his deep academic engagement. His work bridges theoretical innovation and practical engineering applications in robotics and precision mechanisms.

🧾Author Profile

🏆 Strengths for the Award

1. Deep Specialization in Parallel Manipulators

Dr. Yang’s research is highly focused and impactful in the domains of kinematics, stiffness modeling, dynamics, and optimization of parallel manipulators. His work addresses both theoretical foundations and practical design aspects, which are critical to modern robotics and automation systems.

2. Robust and Consistent Publication Record

Since 2018, Dr. Yang has published 20+ peer-reviewed articles, many in high-impact international journals such as:

  • Mechanism and Machine Theory

  • Mechanical Sciences

  • Chinese Journal of Mechanical Engineering

  • Robotica

  • International Journal of Control

3. Novel Contributions in Modeling Techniques

Dr. Yang has proposed and validated novel methodologies including:

  • Elastodynamic and elastostatic modeling of over-constrained systems

  • Use of neural networks and principal component analysis for multi-objective optimization

  • Finite-time tracking control techniques for underwater vehicles
    These show innovation, interdisciplinary thinking, and a mastery of mechanical system modeling.

4. International Journal Reviewership

Dr. Yang serves as a reviewer for journals like Applied Mathematical Modelling and Mechanism and Machine Theory, which reflects recognition of his expertise by the academic community.

🎓 Education 

Dr. Chao Yang began his academic journey in mechanical disciplines with a Bachelor’s degree in Process Equipment & Control Engineering from Zhengzhou University of Light Industry, China, in 2005. He continued to sharpen his analytical and mathematical expertise by completing his Master’s in Engineering Mechanics at Dalian University of Technology in 2009, where he laid the groundwork for his future in dynamic systems and mechanical modeling. In 2019, he earned his Ph.D. in Mechanical Engineering from Zhejiang Sci-Tech University, focusing on advanced dynamic analysis and optimization techniques for parallel robotic manipulators. His academic training integrates control theory, mechanical design, and computational modeling—making him uniquely positioned to tackle cutting-edge problems in modern robotics. This rich educational background directly contributes to his current research, which blends multi-body dynamics, elastostatics, and AI-based optimization in robotic mechanisms.

🔬 Research Focus On Mechanical Engineering

Dr. Chao Yang’s research focuses on the mechanics, modeling, and optimization of parallel manipulators—key elements in robotics and precision automation. His work revolves around four core themes: kinematics, stiffness modeling, dynamic analysis, and multi-objective optimization. He explores how over-constrained or hybrid manipulator systems can be optimized using neural networks, principal component analysis, and evolutionary algorithms. His innovative modeling methods extend to both elastostatic and elastodynamic domains, enabling more precise and adaptive control systems. He also delves into applications such as underwater robotics and hybrid robot platforms. By avoiding Lagrangian multipliers in modeling and adopting screw theory in kinetostatic design, he simplifies computational complexity while maintaining physical accuracy. His contributions fill a crucial gap in designing robust, high-performance robotic systems that are used in manufacturing, aerospace, and intelligent automation. Dr. Yang’s research is practical, interdisciplinary, and driven by the demands of next-generation robotics.

📚 Publications Top Notes

1. A hybrid algorithm for the dimensional synthesis of parallel manipulators

Journal: Proc. IMechE Part C: Journal of Mechanical Engineering Science, 2025
Authors: Yang, C.; Zhang, H.; Huang, F.; Ye, W.
Summary: This study presents a novel hybrid algorithm integrating evolutionary computing and deterministic search for optimizing the geometry of parallel manipulators. It addresses trade-offs in workspace, stiffness, and dexterity with improved computational performance.

2. Elastodynamic modeling and analysis of a 4SRRR overconstrained parallel robot

Journal: Mechanical Sciences, 2025
Authors: Wang, B.; Zhao, Y.; Yang, C.; Hu, X.; Zhao, Y.
Summary: Investigates vibration and dynamic response of a 4SRRR parallel robot. The study contributes to better understanding structural deformation under motion, crucial for high-speed precision applications.

3. Kinematic Analysis and Optimization Design of 2-PRU-PRRPa Parallel Mechanism

Journal: Transactions of the Chinese Society for Agricultural Machinery, 2025
Authors: Zhang, W.; Feng, S.; Yuan, X.; Sun, P.; Yang, C.; Lu, Y.
Summary: Offers a systematic study of a novel parallel mechanism applied in agricultural automation, optimizing motion paths and actuator placement.

4. Multibody elastodynamic modeling of parallel manipulators based on the Lagrangian equations without Lagrangian multipliers

Journal: Proc. IMechE Part C: Journal of Mechanical Engineering Science, 2025
Authors: Gong, Y.; Lou, J.; Yang, C.; Ye, W.
Summary: This paper introduces a novel elastodynamic modeling approach for parallel manipulators that bypasses the use of Lagrangian multipliers. The methodology improves numerical efficiency and simplifies model derivation, making it suitable for real-time control and simulation of complex parallel robotic systems.

5. Dynamic modeling and performance analysis of the 2PRU-PUU parallel mechanism

Journal: Mechanical Sciences, 2024
Authors: Sun, T.; Ye, W.; Yang, C.; Huang, F.
Summary: Focuses on the dynamic modeling of a 2PRU-PUU architecture parallel robot. Through simulation and performance metrics evaluation, the study demonstrates how structural configurations affect system response and highlights its suitability for precision tasks in constrained workspaces.

Conclusion

Dr. Chao Yang is highly suitable for the Research for Best Researcher Award—particularly in domains of mechanical systems design, parallel robots, and multi-objective optimization. His contributions are academically rich, technically deep, and steadily expanding. While early in his career stage as a lecturer, the maturity and depth of his publication portfolio, coupled with innovative methodologies, clearly reflect a rising star in mechanical engineering research.