Dr. Tao’s greatest professional joy is helping students learn and develop engineering concepts and their industrial applications. He participates in designing and developing future technologies through the students’ achievements. His interdisciplinary science background allows me to prepare students in multiple disciplines, mainly mechanical, electrical, control, and robotics engineering. Dr. Tao’s teaching strategy is to focus on student-centered instruction, implemented to the best of my ability. He believes what’s important is “not what is being taught to students, but what is being learned by students.” Dr. Tao prefers to guide the students with projects that allow students hands-on experience solving real-world problems. Through these projects, students’ learning curve increases drastically. Students develop a deeper understanding of the principles taught and how to apply them.

Future Courses

MERO 5333 Learning-Based Control for Mechatronics and Robotics (Fall 2024)

The goal of this course is to give the students an introduction to a variety of intelligent control techniques and their applications in mechatronics and robotics systems.

Current Courses

OSU MET 3803 Fundamentals of Mechatronics (Spring 2024)

OSU MERO 5113 Mechatronic Systems I (Spring 2024)

Fundamentals of mechatronic systems and components. Different modeling approaches are used for mechatronics systems, sensors and actuators, data acquisition and interfacing, signal conditioning, and PLCs.

Past Courses

OSU MERO 5313 Linear Control Systems for Mechatronics (Fall 2023)

The course is an application-specific course. Applications of feedback control in mechatronics, mathematical models of mechatronics systems and components, time-domain analysis, and stability, and state-variable models of feedback systems.

Mines MEGN 545 Advanced Robot Control (Spring 2021, 2022, 2023)

The goal of this course is to give the students an introduction to a fundamental working knowledge of the main techniques of intelligent learning-based control and their applications in robotics and autonomous systems. Specific topics include neural network-based control, model predictive control, reinforcement learning-based control, fuzzy logic control, and human-in-the-loop control.