Pezhman Hassanpour, PhD, PE

As a teacher-scholar, my work focuses on applying scientific and engineering principles, along with cutting-edge technologies, to address real-world challenges. I firmly believe that research and teaching are deeply interconnected, with each enriching the other. My primary area of interest is dynamic systems and control, an inherently interdisciplinary field that continually draws me to explore other domains of engineering, especially electrical and digital systems. Over the years, I have designed and implemented numerous advanced electronic circuits, tailoring them to control or evaluate the behavior of various mechanical systems.

The following are the current subareas of my research:

• Design and Analysis of Micro-electromechanical Systems (MEMS)
• Vibration and Control of Dynamic Systems
• Application of Machine Learning and Artificial Intelligence in Dynamic Systems
• Sensor Fusion and Autonomous Robotic Systems
• Dynamic systems governed by hypergeometric differential equations

Design and Analysis of Micro-electromechanical Systems (MEMS)

My primary research interest lies in the design of MEMS devices, with a particular emphasis on dynamic systems. A substantial portion of my work has involved investigating resonant micro-structures. Certain micro-resonators are engineered to oscillate at designated frequencies, serving applications such as timing and inertial measurement units. In contrast, other micro-resonators utilize frequency shifts as a mechanism for quantifying physical parameters like pressure, force, or mass changes; these are often referred to as resonant sensors. My current focus is on enhancing the performance of micro-resonators by reducing their susceptibility to noise, including thermal noise, and by exploring the utilization of nonlinear phenomena.

Vibration and Control of Dynamic Systems

Within this area, my main research interests focus on vibration and control of systems. I am particularly engaged in designing and implementing control strategies for inherently unstable plants, such as magnetic levitation systems and inverted pendulums with reaction wheels, using modern control theory. Additionally, I have explored the use of signal processing techniques to characterize both linear and nonlinear dynamic systems in this field.

Application of Machine Learning and Artificial Intelligence in Dynamic Systems

My initial experience with artificial intelligence dates back to 2004, when I applied it to predict the nonlinear behavior of beams with soft support. More recently, I have utilized machine learning and AI technologies for device development, focusing on cost-effective microcontrollers such as various Arduino boards. I have designed and built devices like optical biosensors employing these microcontrollers. Central to these devices is an ML/AI-based processing unit that receives raw sensor data, classifies the type, and quantifies the measurand.

Sensor Fusion and Autonomous Robotic Systems

Recently, I began working on sensor fusion techniques with a focus on applications in autonomous robotic systems. This research is currently being used to develop an autopilot system for drones designed for the SAE Aero Design West advanced class competition. Sensor fusion has a variety of applications across different technological fields and is closely integrated with the use of machine learning and artificial intelligence.

Dynamic Systems Governed by Hypergeometric Differential Equations

This part of my research explores theoretical solutions for dynamic systems with complex differential equations, typically addressed through numerical methods. With proper transformation, some of these equations of motion can be expressed as hypergeometric differential equations. The solutions of such equations require careful consideration of the parameters of the original physical systems; however, the outcome exhibits remarkable numerical efficiency over the conventional numerical time-domain integrations.