Although the beauty of control theories attracts many engineers, the inherent limitations of control theories are unavoidable in practice. Most of the control theories assume that a plant is given and seek a solution to obtain the desired performance by suggesting an appropriate control algorithm. EXO-Lab’s research issues are all based on a fundamental question that the application of control theories should start from the beginning of mechanical design. Namely, the mechanical system should be designed considering the controllability and observability of the plant, such that the system has the minimal limitations in the controller design. Also, the controller design process should suggest both control parameters and mechanical design parameters for obtaining the most effective and energy-efficient control performance. Therefore, students of EXO-Lab learn both the mechanical design and control theories in an integrated way. This education philosophy may be different from typical educations in the field of Mechatronics or Robotics in a viewpoint that it deals with the interdisciplinary cross-application of the mechanical design and control theories.

This course introduces fundamental concepts and methodologies in human assistive robotics, with a focus on robotic systems designed to augment, rehabilitate, or assist human motion. Human assistive robots must interact seamlessly with human biomechanics, which requires an understanding of human motion mechanisms, biomechanical modeling, and bio-signal processing. The course first examines human motion mechanisms, including muscle, tendon, and joint dynamics, as well as theories of human motion control. It then addresses the mechanical design of assistive robots, taking human anatomy and movement mechanisms into account. The integration of bio-signals, such as EMG and EEG, into robotic control systems is also introduced, along with analytical methods for motion analysis. Finally, the course covers control strategies for assistive robots, including state estimation, motion trajectory generation, force-based control methods, and human-in-the-loop optimization. Through theoretical instruction and practical demonstrations, students gain a comprehensive understanding of the design and control of human assistive robotic systems.

This course introduces fundamental concepts for the control of linear systems. The term “linear system” refers not only to ideal systems described by equations of motion, but also to practical mechanical systems whose dominant dynamic behavior can be approximated as linear. To address such systems, the course begins with linear system theory for modeling and analysis. Core topics in linear control theory are then covered, including state-space representations, controllability and observability, transfer functions, time-domain analysis, and stability. Since real-world systems inevitably exhibit uncertainties in their dynamic characteristics, the course also introduces robust control methods, such as the small gain theorem and H-infinity optimal control, to handle systems with uncertain dynamics. Various numerical and mathematical techniques for control algorithm design are presented throughout the course.

This course provides a comprehensive introduction to control system synthesis in which digital computers play a central role. The course covers key elements of real-time computer architecture, input–output interfaces, and data converters, as well as the analysis and synthesis of sampled-data control systems using both classical and modern state-space methods. It also examines trade-offs in control algorithm design related to computation speed and quantization effects. In addition to foundational theory in digital control systems, advanced topics such as discrete-time linear quadratic control, adaptive control, parameter adaptation, repetitive control, and learning control are introduced. Coursework includes computer-based simulations that allow students to experience practical issues in digital servo implementation, including timing constraints, noise, and other real-world challenges.