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카테시안 공간에서 시간 지연제어기의 구현에 대한 튜토리얼
정슬 제어·로봇·시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.6
This paper presents a tutorial on the implementation of the time-delayed controller in the Cartesian space for robot manipulators. Although the Cartesian space position is actually controlled in the joint space through the inverse kinematics, the direct control of the Cartesian position is required for force control applications. For the implementation of Cartesian space controllers, there are several schemes due to the requirement of the transformation from the Cartesian space to the joint space. One is to use the acceleration relation between the joint space and the Cartesian space, which requires the inverse and the derivative of the Jacobian. Another is to use the transpose of the Jacobian. Accordingly, the time-delayed controller can be constructed in either the joint space or the Cartesian space for the Cartesian space control. Advantages of each implementation of the time-delayed controller for the Cartesian space are discussed. Extensive simulation studies for a robot manipulator to follow a circular trajectory are performed to explain the advantages of implementation of the time-delayed controller in the Cartesian space. .
정슬 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.7
A time-delayed control (TDC) method is known as a simple, robust and non model-based control scheme that requires the fast sampling time, the accurate measurement of joint acceleration signals, and the accuracy of the inertia model of a robot manipulator. Among them, sampling time and acceleration signals are hardware dependent and can be solved. Then a user specified inertia model becomes a key role for the performance of TDC. When the selection of the diagonal element of the inertia matrix of a robot manipulator is used, the ill selection of the constantinertia matrix may lead to the poor tracking performance as well as instability. In addition, an appropriate selection of an inertia matrix for different tasks of the robot is not easy. Therefore, in this paper, an intelligent way of using a neural network is proposed to compensate for the deviation of the constant inertia matrix of a robot manipulator. The role of the neural network is to improve the tracking performance of a robot manipulator by compensating for the deviated error of the inertia matrix while satisfying the stability bound. Simulation studies of a three link robot. are presented to confirm the proposal.
정슬 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.2
Neural network control for robot manipulators is aimed to compensate for uncertainties in the robotdynamics. The location of a compensating point differentiates the control scheme into two categories, the feedbackerror learning (FEL) scheme and the reference compensation technique (RCT). The RCT scheme is relatively lessused although it has several structural advantages. In this paper, the global stability of the RCT scheme is analyzedon the basis of Lyapunov function. The analysis turns out that the stability depends upon the magnitude of thecontroller gains. Simulation studies of controlling the position of a two-link robot manipulator are conducted.
정슬 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2
This article presents a neural network control technique to improve the tracking performance of a robot manipulator controlled by the sliding mode control method in a non-model-based framework. The sliding mode controller is a typical nonlinear controller that has been well developed in theory and used in many applications due to its simplicity and practicality. Selection of the gain of the nonlinear function plays an important role in performance as well as stability. When the sliding mode controller is used for the non model-based configuration in robot control, the nonlinear gain should be selected large enough to guarantee the stability. Since the appropriate selection of the gain value is essential and difficult in the sliding mode control framework, a neural network compensator is introduced at the trajectory level to help the fixed gain deal with the stability and performance more intelligently. Stability of the proposed control scheme is analyzed. Simulation studies of following the Cartesian trajectory for a three-link rotary robot manipulator are conducted to confirm the control improvement by the neural network.
Development of a Creative Robot School Program for Motivating Elementary School Students
정슬 한국공학교육학회 2011 공학교육연구 Vol.14 No.3
This article presents program development and analysis of a creative robot school for elementary school at the local university. The purpose of opening the creative robot school is to give motivation to children for having interests in science and engineering at their young ages. The creative robot school program is developed by using facilities of a local university to spread scientific knowledge to young children in their communities to draw their interests in science as well as an engineering field for future careers. Since the robot system is a popular subject to draw attention of children and has a relation with Mechatronics Engineering, a program related with robots is selected for educating children. College students are also involved in helping children to build robots within a given time. Experiences and self-evaluations from the previously held creative robot schools at Chungnam National University(CNU) are presented to share with.
정슬,T. C. Hsia 대한임베디드공학회 2011 대한임베디드공학회논문지 Vol.6 No.3
This paper presents an intelligent control approach for lateral position control of an autonomous four wheel steered snowplowing robotic vehicle. The vehicle is built for removing snow on the highway. Dynamics of the vehicle is derived and linearized for LQR control. Lateral position is controlled by the LQR method first, then the neural network control technique is introduced to improve tracking performances under the presence of load. The feasibility of using four wheel steering control is investigated by simulation studies of lateral position tracking of the Ford F-250 truck model. Performances of a LQR control method and a neural network control method under virtual snowplowing situation are compared.