To obtain high positioning auccuracy for a long, flex bleman Ipulator, residual vibration must be removed from the tip motion. But it is difficult to control the vibration of low frequency. There are open-loop and closed loop methods in the elimination of the residual vibration. We inroduce input shaping technique has been used as a simple open-loop method of controlling the residual vibration of a flexible manipulator. Design of input shaper in the continuous time domain is complicated. This paper presents a new technique that designs input shaper in the z-domain and analyzes input shaping method in the z-domain. This technique is simple and easy to design input shaper.

This paper deals with a problem searching a target transfer function to meet the time-domain specifications for feedback system with given plant transfer function. For the Type I system, we first define three forms of transient response to unit step input, which are named by F, M, S-type. These are charaacterized as follows ; F-type has fast initial response and slow approach to the steady sate after reaching at 90% of the steady state value, S-type has slow initial response but fast approach to the steady state, and M-type is denoted by highly smooth response between F-type and S-type. Three prototypes corresponding to each form are proposed, time. For the order $n{\geq}4$, after determining admissible root structures of target characteristic polynomials empirically and expressing such polynomial coefficients by using special parameters ${\gamma}_i$ and $\epsilon$, the optimal prototypes that minimize the integral of the squared of the modified errors(ISME) have been obtained. Since the step responses of these prototypes have almost same wave forms irrespective to the order, the desired settling time or the rise time can be converted into the equibalent time constant $\tau$ and thus it is easy to obtain a target transfer function. It is shown through a design example that the present prototype is very useful for meeting the time-domain specifications and has been compared with different methods with a viewpoint of pertinence.

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

For an automated transportation system like PRT(Personal Rapid Transit) system or IVHS, an efficient vehicle-merging algorithm is required for smooth operation of the network. For management of merging, collision avoidance between vehicles, ride comfort, and the effect on traffic should be considered. This paper proposes an unmanned vehicle-merging algorithm that consists of two procedures. First, a longitudinal control algorithm is designed to keep a safe headway between vehicles in a single lane. Secondly, 'vacant slot and ghost vehicle' concept is introduced and a decision algorithm is designed to determine the sequence of vehicles entering a converging section considering energy consumption, ride comfort, and total traffic flow. The sequencing algorithm is based on fuzzy rules and the membership functions are determined first by an intuitive method and then trained by a learning method using a neural network. The vehicle-merging algorithm is shown to be effective through simulations based on a PRT model.

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

In this paper, a modeling and a robust time-delay control for the reclaimer are investigated. Supplying the same amount of a raw material throughout the reclamation process from the raw yard to a sinter plant is important to keep the quality of the molten steel uniform in blast furnaces. As the actual parameter values of the reclaimer are not available, the boom rotational dynamics are modeled as a second order differential equation with unknown coefficients. The unknown parameters in the nominal model are estimated using a recursive estimation method. Another important factor in the control design of the reclaimer is the large time-delay in output measurement. Assuming a multiplicative uncertainty, that accounts for both the unstructured uncertainty neglected in the modeling and the structured uncertainty contained in the parameter estimation, a robust Smith predictor is designed. A robust stability criterion for the multiplicative uncertainty is also derived. Following the work of Goodwin et al. [4], a quantifying procedure of the multiplicative uncertainty bound, through experiments , is described. Experimental and simulation results are provided.

In this paper, a new neural networks and neural network based sliding mode controller are proposed. The new neural networks are an mor self-recurrent neural networks which use a recursive least squares method for the fast on-line leammg. The error self-recurrent neural networks converge considerably last than the back-prollagation algorithm and have advantage oi bemg less affected by the poor initial weights and learning rate. The controller for suspension system is designed according to sliding mode technique based on new proposed neural networks. In order to adapt shding mode control mnethod, each frame dstance hetween ground and vehcle body is estimated md controller is designed according to estimated neural model. The neural networks based sliding mode controller approves good peiformance throllgh computer sirnulations.

As pressure transducers are employed in many fields such as production facilities, test facilities vehicles and industrial machinery, there is an increasing need for high precision measurement of pressure without any calibration or maintenance. In this paper, we discuss the development of a smart thin film pressure transducer which is highly suitable for a precise measurement of pressure. The smart functions include automatic zero tracking, automatic span adjustment, temperature compenstion, continuous self-diagnostics for faults (open strain gages, abnormal data, incorrect A/D conversion, and overpressure), data memory and multi-drop communication with PC

In this paper, we propose a cooperative motion planning algorithm for two tightly-coupled mobile robots. Specifically, the considered cooperative work is that two mobile robots should transfer a long rigid object along a predefined path. To resolve the problem, we introduce a master-slave concept for two obile robots having the same structure. According to the velocity of the master robot and the positions of two robots on the path, the velocity of the slave robot is determined. The slave normally tracks the master's motion, but in case that the velocity of the slave exceeds the velocity limit, the roles of the robots should be interchanged. The effectiveness of the proposed algorithm is proved by computer simulations.

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.

In this paper we handle a system that transform sensor data to sensor information. Sensor informations from redundant accelerometers are manipulated to represent the configuration of objects carrying sensors. Basic sensor unit of the proposed systme is composed of 3 accelerometers that are aligned along x-y-z coordination axes of motion. To refine the sensor information, at first the sensor data are fused by geometrical optimization to reduce the variance of sensor information. To overcome the error caused from inexact alignment of each sensor to the coordination system, we propose a calibration technique that identifies the transformation between the coordinate axes and real sensor axes. The calibration technique make the sensor information approach real value. Also, we propose a technique that decomposes the accelerometer data into motion acceleration component and gravity acceleration component so that we can get more exact configuration of objects than in the case of raw sensor data. A set of experimental results are given to show the usefulness of the proposed method as well as the experiments in which the proposed techniques are applied to human body motion capture.

This paper is concerned with the generation of a balancing trajectory for improving the walking performance. The balancing motion has been determined by solving a second -order differential equation. However, this method caused some difficulties in linearizing and approximating the equation and had restrictions on using various balancing trajectories. The proposed difficulties in linearizing and approximating the equation and had restrictions on using various balancing trajectories. The proposed method i this paper is based on the genetic algorithm for minimizing the motins of balancing joints, whose trajectories are generated by the fifth-order polynomial interpolation after planning leg trajectories. The real walking experiments are made on the biped robot IWR-III, developed by our Automatic Control Laboratory. The system has 8 degrees of freedom and the structure of three pitches in each leg, and one roll and one prismatic joint in the balancing joints. The experimental result shows the validity and applicability of the new proposed algorithm.

The purpose of this study is to analyze how the human body having more muscles than its degree-of-freedom modulates an effective stiffness using redundant actuation, and to apply this concept to the design and control of advanced machines which requires adaptable spring. To investigate the adaptable stiffness phenomenon due to redundant actuation in the human body, this paper derives a general stiffness model of the Human body. In particular, for a planar 1 DOF human arm model, a planar 2 DOF human arm model, a spherical 3 DOF shoulder model, a 4 DOF human arm model, and a 7 DOF human arm model, the required nonlinear geometry ad the number of required actuator for successful modulation of the effective stiffness are analyzed along with a load distribution method for modulation of the required stiffness of such systems. Secondly, the concept of motion frequency modulation is introduced to show the usefulness of adaptive stiffness modulation. The motion frequency modulation represents a control of stiffness and / or inertia properties of systems. To show the effectiveness of the proposed algorithm, simulations are performed for 2 DOF anthropomorphic robot.

This paper proposes a new fuzzy-neural algorithm for navigation of a mobile robot with stationary and moving obstacles environment. The proposed algorithm uses fuzzy algorithm for its speed control and neuralnetwork for effective collision avoidance. Some computer simulation results for a mobile robot equipped with ultrasonic range sensors show that the suggested navigation algorithm is very effective to escape in stationary and moving obstacles environment.

This paper presents a optic system and a visual inspection algorithm looking for solder joint defects of J-lead chip which are more integrate and smaller than ones with Gull-wing on PCBs(Printed Circuit Boards). The visual inspection system is composed of three sections : host PC, imaging and driving parts. The host PC part controls the inspection devices and executes the inspection algorithm. The imaging part acquires and processes image data. And the driving part controls XY-table for automatic inspection. In this paper, the most important five features are extracted from input images to categorize four classes of solder joint defects in the case of J-lead chip and utilized to a back-propagation network for classification. Consequently, good accuracy of classification performance and effectiveness of chosen five features are examined by experiment using proposed inspection algorithm.