http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
정슬 제어·로봇·시스템학회 2024 제어·로봇·시스템학회 논문지 Vol.30 No.4
. This paper presents historical perspectives on the force control algorithms for robot manipulators to deal with environment. From the compliance control for a robot manipulator to deal with the environment to bilinear force control, a variety of force control algorithms are presented with their characteristics. The most important issue of force control is how to deal with environment and ensure force tracking performance. Problems of which force control algorithms should solve are unknown environment stiffness and position, force tracking capability, and uncertainties in robot dynamics. Analysis on how to deal with those problems by major force control algorithms is elaborated. Finally, a new force control algorithm, bilinear force control is presented to overcome the aforementioned problems.
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.
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.
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.
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.
Neural Network Compensation for Impedance Force Controlled Robot Manipulators
정슬 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.1
This paper presents the formulation of an impedance controller for regulating the contactforce with the environment. To achieve an accurate force tracking control, uncertainties inboth robot dynamics and the environment require to be addressed. As part of the frameworkof the proposed force tracking formulation, a neural network is introduced at the desiredtrajectory to compensate for all uncertainties in an on-line manner. Compensation at the inputtrajectory leads to a remarkable structural advantage in that no modifications of the internalforce controllers are required. Minimizing the objective function of the training signal for aneural network satisfies the desired force tracking performance. A neural network actuallycompensates for uncertainties at the input trajectory level in an on-line fashion. Simulationresults confirm the position and force tracking abilities of a robot manipulator.