Education
  • 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 know-how 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 energy-efficient 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 cross-application 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, closed-loop limitations imposed by an open-loop 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 real-world 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, hands-on 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 non-stochastic signal filtering, automatic parameter adaptation methods, design of input signals, and so on. For students to obtain the hands-on 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 curve-fit, 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 hands-on 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.