http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
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 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 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.
Guidance Control of a Wheeled Mobile Robot with Human Interaction Based on Force Control
정슬,이형직 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.2
This paper presents a mobile robot carrier designed to carry a person using two modes: a mechanism with full support and another with partial support. The carrier is driven through guided control from an operator. Applied force is sensed by a force sensor mounted on the bottom of the handle. The measured force is filtered by the impedance function that generates the desired velocity to drive the motors. The inner loop PID controller is then required to follow the desired velocity, which is the reference input to the system. The impedance function is designed to make the driving condition comfortable for the driver by smoothing out abrupt starts and stops. Feasibility tests on the application of the impedance force control method to the carrier robot have been performed through experimental case studies aimed at evaluating the comfort level of prospective users: one is on a full support case when a user is riding on the carrier and another on a partial support case where the user is pushing the carrier.
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.
Position Control of a Mobile Inverted Pendulum System Using Radial Basis Function Network
정슬,노진석,이근형 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.1
This article presents the implementation of position control of a mobile inverted pendulum (MIP) system by using the radial basis function (RBF) network. The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is a nonlinear system whose dynamics is non-holonomic. The goal of this study was to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique scheme is used as a neural network control method for the MIP. The back-propagation learning algorithm of the RBF network is derived for online learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the desired position of the MIP.