This course introduces variable structure control (VSC) theory which is one of the robust control theories. Various basic theorems of VSC will be analyzed in the sliding mode. Expanding the target plant from a second order plant to the n-th order plant, it will be studied how to determine switching conditions and switching vectors. Stability will be analyzed by designing a feedback control loop. By integrating multi-variable structure with optimal control theory and adaptive control theory, the problem of system optimization and the problem of determining coefficients of switching vector in sliding mode will be resolved. Based on those theories, discrete variable structure control (DVSC) will be introduced. Finally, it will be studied how to apply those theories to the control system in robot systems, space aerial planes, satellites, chemical plants, power plants and motors.
This course is intended to present the fundamental result of analysis and design of nonlinear control systems. Especially, this course is concerned with the analysis tools for nonlinear dynamical systems and the design techniques for nonlinear control systems. (Prerequisite: EE581)
Among the various well-known intelligent control techniques, the methods of fuzzy control and neural net-based learning control are first introduced to allow for handling ambiguous/uncertain situations and effective supervised learning, respectively. Specifically, the theory of fuzzy sets and fuzzy logic-based inference mechanism are studied and the design techniques of fuzzy control are introduced. Then, the neural net learning structure is discussed and the control system based on artificial neural nets is studied. Fuzzy-neuro systems are also considered. In the second part of the course work, some other computational intelligence techniques such as GA and the rough set are briefly covered and then the basic machine learning techniques and the reinforcement learning method are studied in conjunction with their use in control system design. (Prerequisite: EE581)
This course is intended to cover kinematics, dynamics and control algorithm of a robot manipulator. After covering homogeneous transformations, kinematics equations, motion trajectory planning, we will handle various control methods. We will compare the utilization of these control methods through simulation.
This course deals with the derivation of maximum principle and the design of optimal control system. It includes an optimal design method for minimum time and energy along with dynamic programming and discrete maximum principle. Also advanced topics of optimal control are introduced. (Prerequisite: EE581)