• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.2
• /
• pp.96-101
• /
• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.712-715
• /
• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1909-1914
• /
• 2004

Passive velocity field control (PVFC) was previously developed for fully mechanical systems, in which the motion task was specified behaviorally in terms of a velocity field, and the closed-loop was passive with respect to a supply rate given by the environment input. However the PVFC was only applied to a single manipulator, the proposed control law was derived geometrically, and the geometric and robustness properties of the closed-loop system were also analyzed. In this paper, we propose a method to apply a decentralized control algorithm to cooperative 3-wheeled mobile robots whose subsystem is under nonholonomic constraints and which convey a common rigid object in a horizontal plain. Moreover it is shown that multiple robot systems ensure stability and the velocities of augmented systems convergence to a scaled multiple of each desired velocity field for cooperative mobile robot systems.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.30 no.4
• /
• pp.329-340
• /
• 1994

This paper presents a dynamic compensation methodology for robust trajectory tracking control of uncertain robot manipulators. To improve tracking performance of the system, a full model-based feedforward compensation with continuous VS-type robust control is developed in this paper(i.e,. robust decentralized adaptive control scheme). Since possible bounds of uncertainties are unknown, the adaptive bounds of the robust control is used to directly estimate the uncertainty bounds(instead of estimating manipulator parameters as in centralized adaptive control0. The global stability and robustness issues of the proposed control algorithm have been investigated extensively and rigorously via a Lyapunov method. The presented control algorithm guarantees that all system responses are uniformly ultimately bounded. Thus, it is shown that the control system is evaluated to be highly robust with respect to significant uncertainties.

• Journal of the Korean Society of Industry Convergence
• /
• v.18 no.2
• /
• pp.119-128
• /
• 2015

In this paper, we propose a new motion control technology to design robust control system of industrial robot. The system modeling of robotic manipulation tasks with constraints is presented, and the control architecture for unconstrained and constrained motion system with parametric uncertainties is synthesized. The optimal reference of robot manipulator is generated by the reference controller as a discrete state system and the control behavior of constrained system which has poor modeling information and time-invariant constraint function is improved motion control system is successfully evaluated by experiment to the desired tasks.

• Journal of Biomedical Engineering Research
• /
• v.28 no.3
• /
• pp.363-368
• /
• 2007

Most surgery illumination systems have been developed as passive systems. However, sometimes it is inconvenient to relocate the position of the illumination system whenever the surgeon changes his pose. To cope with such a problem, this study develops an auto-illumination system that is autonomously tracking the surgeon's movement. A 5-DOF serial type manipulator system that can control (X, Y, Z, Yaw, Pitch) position and secure enough workspace is developed. Using 3 ultrasonic sensors, the surgeon's position and orientation could be located. The measured data aresent to the main control system so that the robot can be auto-tracking the target. Finally, performance of the developed auto-illuminating system was verified through a preliminary experiment in the operating room environment.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.283-288
• /
• 1994

The adaptive control and the robust control have been considered as the most influential methods for robotic motion control. The purpose of this paper is to compare control performance between these two strategies in unconstrained motion control of robot manipulator. In order to compare control performance properly, intensive experiments are required and only then can conclusions be drawn on the relative merit and demerit of the controllers. Firstly, the control algorithms for unconstrained motion control are summarized. In adaptive control, the controllers that have been proposed so far are classified according to the signals used for the computed control input. It enables rather easier to compare controller is examined to demonstrate control performance of robust controllers. Finally, the above two approaches, the adaptive and the robust are compared from the view-point of robustness to plant uncertainty, which is one of the most demanding properties in robot motion control.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.302-305
• /
• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.8
• /
• pp.955-960
• /
• 1999

This paper describes the effect of impact configurations on a single robot manipulator. The effect of different configurations of kinematically redundant arms on impact forces at their end effectors during contact with the environment is investigated. Instead of the well-known impact ellipsoid, I propose an analytic method on the geometric configuration of the impact directly from the mathematical definition. By calculating the length along the specified motion direction and volume of the geometry, we can determine the characteristics of robot configurations in terms of both the impact along the specified direction and the ability of the robot withstanding the impact. Simulations of various impact configurations are discussed at the end of this paper.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10a
• /
• pp.886-891
• /
• 1987

In this paper, we present a effective method to control robotic systems by an iterative learning algorithm. This method is based on the concepts of the learning control law which is introduced in this paper, that is, avoidance of using derivative of system state and ignorance of high frequency influence in system performance. By means of the betterment of performance due to the improvement of estimated unknown information, the learning control algorithm compels the system to gradually approach in desired trajectory, and eventually the tracking error asymptotically converges upon zero. In order to verify its utility, one degree of freedom of manipulator has been used in the experiments and the results illustrate this control scheme is very effective.