 Academic Background
 RSC Lab is based on academic fundamentals of Dynamic
Systems and Control, and students are mostly interested in and inspired from
various control theories, in particular linear system theories and robust
control theories with applications of robotic systems. The knowledge in the
control theories provides students with a solid foundation for robotics
research. As the major applications of the laboratory are robotic systems
interacting with humans, where the control performance is evaluated not only by
typical metrics in the control theory (e.g., tracking performance, robust
stability, etc.) but also by clinical verification, as well as the qualitative
judgement of human subjects, research topics of RSC Lab have focused on the
practicality and implementability of control methods. Consequently, students and
laboratory members could obtain extensive knowhow and expertise on application
of the control theories. Prof. Kyoungchul Kong, the director of RSC Lab,
emphasizes that understanding practical examples and connecting the control
theories with appropriate applications are the best way to educate the next
generation engineers and researchers in the field of Robotics, Dynamic Systems
and Control.
 Teaching Philosophy
 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. RSC 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 energyefficient control performance. Therefore, students of RSC 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 crossapplication of the mechanical design and control theories.
 Prof. Kyoungchul Kong, the director of RSC Lab, gives the following lectures.
 Graduate Courses
 Robust Control Systems
 The major topics of this lecture include the mathematical derivations and analyses of Ricatti equations for optimal control, definitions of robust stability and robust performance, closedloop limitations imposed by an openloop system, modeling of systems with uncertainties, µ synthesis and applications, Youla parameterization and all stabilizing controllers, and practical robust control algorithms with examples of disturbance observer and sliding mode control. Fundamental mathematical tools and computer toolboxes (e.g., linear matrix inequalities, nonlinear programming, etc.) are introduced also. To avoid for this lecture to easily become a pure mathematics class while dealing with robust control theories, and an extra effort is made to provide practical problems and experimental results to help students connect the theories with realworld problems.
 Advanced Mechatronics
 This lecture introduces advanced theories and techniques in mechatronics for dealing with electrical devices, measurements, and controls of mechanical systems. Practical examples given in the lecture include actuating systems (e.g., electric motor systems, pneumatic systems, etc.), signal processing techniques, measurement of analog and digital sensor signals, use of electric elements, communication methods, and implementation of various control algorithms. In this course, handson experiences through a set of laboratory sessions are emphasized particularly.
 System Identification and Practice
 • This lecture introduces modern signal processing methods for identifying system parameters and/or physical models from the measured input and output data. The major topics taught in this class include the signal transformation, stochastic and nonstochastic signal filtering, automatic parameter adaptation methods, design of input signals, and so on. For students to obtain the handson experiences of system identification processes, laboratory sessions with electric motor systems and data acquisition boards are provided.
 Undergraduate Courses
 Digital Control Systems
 This lecture covers from the basic linear control theory to the advanced digital control schemes such as linear quadratic control and disturbance observer. In this course, students’ ability is examined not only through examinations but also by experimental results through laboratory sessions. The final term project is to apply the control methods to an electric motor system with unknown model parameters. Students are asked to obtain the frequency responses by themselves, to find the model parameters by a curvefit, to design feedback and feedforward control algorithms with at least five different approaches, and to verify the control performance by experimental results.
 Mechatronics
 In this lecture, mandatory knowledge for designing mechatronic systems are introduced, and an Arduino board is provided to every student for individual handson experience. The examples given to the students include the use of a motor driver and other fundamental electric components, the construction of a motor control system, the implementation of signal processing algorithms, and so on. The final term project is to develop a wheeled inverted pendulum from scratch, and students are evaluated mainly by the experimental results of their own system.
 Mechanical Systems Analysis
 This lecture introduces mathematical methods used in Mechanical Engineering, in particular linear algebra. This course reviews linear algebra covered in Engineering Mathematics, e.g., matrix arithmetic and determinants, vector spaces, eigenvalues and eigenvectors, linear transformations, homogeneous ordinary differential equations, and Fourier series.
 Linear Vibrations
 This is a typical linear vibrations course, which covers fundamentals in mechanics (e.g., linear system theory, Newtonian mechanics, Lagrangian mechanics, and state space realization), frequency and Laplace domain analyses of mechanical vibration systems, free and forced vibrations of lumped mass systems, and vibrations of distributed parameter systems.
