• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.6
• /
• pp.20-27
• /
• 1994

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an improved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing forces coming from the difference between the actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.1042-1046
• /
• 1993

This paper describes a control scheme for a robot manipulator system which uses visual information to position and orientate the end-effector. In this scheme, the position and orientation of the target workpiece with respect to the base frame of the robot are assumed to be unknown, but the desired relative position and orientation of the end-effector to the target workpiece are given in advance. The control scheme directly integrates visual data into the servoing process without subdividing the process into determination of the position and orientation of the workpiece and inverse kinematics calculation. A neural network system is used for determining the change in joint angles required in order to achieve the desired position and orientation. The proposed system can be control the robot so that it approach the desired position and orientation from arbitrary initial ones. Simulation for the robot manipulator with six degrees of freedom will be done. The validity and the effectiveness of the proposed control scheme will be verified by computer simulations.

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.2
• /
• pp.209-214
• /
• 1995

It is very hard to identify the fuzzy rules and tune the membership functions of the fuzzy reasoning in fuzzy systems modeling .We propose a method which canautomatically identify the fuzzy rules and tune the membership functions of fuzzy reasoning simultaneously using artifical neural network. In this model,fuzzy rules are identified by backpropagation algorithm. The feasibility of the method is simulated by a simple robot manipulator.

• Proceedings of the KIEE Conference
• /
• 1989.07a
• /
• pp.675-678
• /
• 1989

This paper proposes two types of the preview algorithms to predict the joint velocities and joint positions, and deals with a control approach using the preview algorithms for the precise trajectory control. Specifically, a predictor an the form of discrete time state equations is proposed based on the robot dynamics model linearized by the computed torque method. And another state predictor is proposed by the beat line fitting in the least square sense, where present joint velocities and positions and several past positions are employed. Then computer simulations are performed for the SCARA robot with two d.o.f. to show the validities of the proposed algothrithms.

• Proceedings of the KIEE Conference
• /
• 1989.11a
• /
• pp.462-466
• /
• 1989

This paper describes the methods of spline low-order polynomial trajectory planning using only a few limited look-ahead knots on the desired trajectory for the real-time computing. Specifically presented are the mixed joint trajectory planning methods which apply linear or LSPB method to initial and finial segments, overlapped cubic spline method to the other segments, where the displacements for initial and finial segments are chosen to be relatively smaller than the displacements for the other, equidistant segments. Experimental and simulation results of these methods show smooth motions and improved path tracking performances compared with any other interpolated joint trajectory planning methods.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.397-400
• /
• 1992

Many dynamic control have been proposed; however, most of them are limited within stage of simulation study. The main reason is that the computations required for inverse dynamics are far beyond the ability of the present commercially available microprocessors. In this paper, In order to achieve real-time processing in robot dynamic control, a parallel processing computer for robot dynamic control is implemented using two transputer. Two transputer compute two degree of freedom robot. The transputer is a special purpose MPU for parallel processing. Transputers are used in networks to build a high performance concurrent system. A network of transputers and peripheral controllers is constructed using point-to-point communication. To gain most benifit from the transputer architecture, the whole system is programmed in OCCAM which is a high level language for concurrent applications. This control algorithm is applied to the RHINO SCARA type manipulator. We could taked about 438.6 microseconds to compute robot dynamic with two-processors.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.394-396
• /
• 1992

This note is concerned with the point to point control of manipulators having elastic joints. We present a PD control algorithm which is adaptive with respect to the gravity and elastic parameters of robot manipulators. While the conventional control law is used, a new adaptive law is used to improve the performance. The proposed controller is shown to be stable. It is Shown that steady-state position error converges to zero through some simulations concerning the manipulator with three revolute elastic joints.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.866-871
• /
• 1991

The multi-layer neural network possesses the desirable characteristics of parallel distributed processing and learning capacity, by which the uncertain variation of the parameters in the dynamically complex system can be handled adoptively. However the error back propagation algorithm that has been utilized popularly in the learning procedure of the mulfi-Jayer neural network has the significant limitations in the real application because of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.1046-1051
• /
• 1991

For the conveyor tracking application of a robot manipulator, a new control scheme is presented. The presented scheme is divided into two stages : the upper one is the motion planning stage and the lower one is the motion control stage. In the upper stage, the nominal trajectory which tracks the part moving in a constant velocity, is planned considering the robot arm dynamics. On the other hand, in the lower level, the perturbed trajectory is generated to track the variation in the velocity of conveyor belt via sensory feedback and the perturbed arm dynamics. In both stages, the conveyor tracking problem is formulated as an optimal tracking problem, and the torque constraints of a robot manipulator are taken into account. Simulation results are then presented and discussed.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.436-440
• /
• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.