In this paper, we present an adaptive observer for multi-output nonlinear systems that include unknown constant parameters and are not necessarily observable. Based on generalized nonlinear observer canonical form, new adaptive observer canonical form is proposed. Sufficient conditions are given for a nonlinear system to be transformed into the proposed adaptive observer canonical form. The existence of the proposed adaptive observer is given in terms of Lyapunov-like condition and SPR condition. An illustrative example is presented to show the design procedure of the proposed method.

This paper deals with the design of a low-order H/sub ∞/ controller by using an iterative linear matrix inequality (LMI) method. The low-order H/sub ∞/ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, the recently developed penalty function method is applied. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. Numerical experiments showed the effectiveness of the proposed algorithm.

It is known that, if an equilibrium of a nonlinear system has a stability property when an external input is frozen, then the property is maintained under the input being slowly varying. In this paper, we show that the same stability property is preserved not only under slowly varying input but also under slowly-varying-average input (which is not actually slowly varying but its ‘average’ is slowly varying) The input is assumed to be periodic and to vary sufficiently fast. We prove the claim by the average theory and some previous results on the slowly varying inputs.

This paper presents the implementation of a crack tracking mobile robot. The crack tracking robot is built for tracking cracks on the pavement. To track cracks, crack must be detected by laser and camera sensors. Laser sensor projects laser on the pavement to detect the discontinuity on the surface and the camera captures the image to find the crack position. Then the robot is commanded to follow the crack. To detect crack position correctly, neural network is used to minimize the positional errors of the captured crack position obtained by transformation from 2 dimensional images to 3 dimensional images.

In this paper, I developed an intelligent multi agents based softPLC(IMPLC). In IMPLC, the standard IEC 1131-3 PLC languages(LD, SFC, FBD, ST) programmed by a user are converted to IL, which is one of intermediate codes, in order to make them interactions. And then the IL is converted to the standard C code regarding some extension and transplanting, which can be used in a commercial editor such as visual C++. In IMPLC, the logical errors and syntax errors occurred by users are detected, so that the optimal PC control based softPLC can be possible. IMPLC provide easy programming platform to such beginner as well as professionals. The study of code conversion is firstly tried in the world as well as KOREA. I applied IMPLC to 3 steps conveyer belt system. The simulation results say that the debugging steps by IMPLC using multi agents are decreased than the conventional softPLC's.

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robot need to recognize his position and direction for intelligent performance in an unknown environment. And the mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this research, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the accurate measurement. As a general approach of sensor fusion, a UT -Based Sensor Fusion(UTSF) scheme using Unscented Transformation(UT) is proposed for either joint or disjoint data structure and applied to the landmark identification for mobile robot navigation. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. The newly proposed, UT-Based UTSF scheme is applied to the navigation of a mobile robot in an unstructured environment as well as structured environment, and its performance is verified by the computer simulation and the experiment.

In this paper an intelligence embedding quadruped pet robot is described. It has 15 degrees of freedom and consists of various sensors such as CMOS image, voice recognition and sound localization, inclinometer, thermistor, real-time clock, tactile touch, PIR and IR to allows owners to interact with pet robot according to human's intention as well as the original features of pet animals. The architecture is flexible and adopts various embedded processors for handling sensors to provide modular structure. The pet robot is also used for additional purpose such like security, gaming visual tracking, and research platform. It is possible to generate various actions and behaviors and to download voice or music files to maintain a close relation of users. With cost-effective sensor, the pet robot is able to find its recharge station and recharge itself when its battery runs low. To facilitate programming of the robot, we support several development environments. Therefore, the developed system is a low-cost programmable entertainment robot platform.

This paper considers the application of mobile robot to the herding problem. The herding problem involves a ‘pursuer’ trying to herd a moving ‘evader’ to a predefined location. In this paper, two mobile robots act as pursuer and evader in the fenced area, where the pursuer robot uses a fuzzy cooperative decision strategy (FCDS) in the herding algorithm. To herd evader robot to a predefined position, the pursuer robot calculates strategic herding point and then navigates to that point using FCDS. FCDS consists of a two-level hierarchy: low level motion descriptors and a high level coordinator. In order to optimize the FCDS, we use the multi­thread evolutionary programming algorithm. The proposed algorithm is implemented in the real mobile robot system and its performance is demonstrated using experimental results.

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

In this paper, experimental studies of force control of the crack tracking robot are presented. The crack tracking robot should maintain constant contact with the road to perform cleaning process of the crack effectively. Regulating desired force on the road requires a sophisticated force control algorithm. Here, two main force control algorithms such as the impedance force control and the explicit force control are used. Performances of two force control algorithms are compared.

The modem production systems are required to produce many items. This is due to the fact that society has become more complex and the customers' demands have become more varied. The demand for complex production systems of various purposes, which can flexibly change the content of work, has increased. One of such production systems is FMS (Flexible Manufacturing System). Limited resources must be used in FMS when a number of working procedures are simultaneously being undertaken because the conditions of stand-by job processes cannot be changed. Researchers are currently being conducted to determine ways of preventing deadlocks. In this study, we proposes the algorithm for detection and recovery of a deadlock status using the DDAPN(Deadlock Detection Avoidance Petri Net). Also, we apply the proposed algorithm has a feature to the FMS.

This article describes a state-of-the art review in nano-biomanipulation technologies. Nanomanipulation of biological objects enables an in-depth study of single molecules such as DNA and RNA, and of biophysical events at the molecular level like molecular motors. Controlled nanomanipulation is challenging but essential for precisely engineering biomolecules or cells and for manufacturing functional nano-biosystems. In this paper, we summarize several contact, non-contact and hybrid methods available for nanomanipulation of biological objects. Advantages currently available methods and their limitations are also compared. Finally, we discuss possible applications of nano-biomanipulation technologies to life science and molecular medicine including cell biology, genetic engineering, biophysics, and biochemistry.

This paper proposes an LOS(line-of-sight) guidance system of a ship for path following. From the viewpoint of a control configuration, guidance is a special type of compensation algorithm that is placed in front of the controller to accomplish navigational objects. A guidance system generates a reference trajectory for trajectory tracking or path control and decides the desired velocity, position and heading angle. A control system executes commands based on a reliable guidance law during navigation. An LOS vector from the vessel to a point on the path between two way-points in straight-line navigation or a point among turning circle in turning navigation is selected, and then a heading angle is calculated to converge the desired path based on the LOS vector. The LOS guidance law is defined for the straight-line and the turning circle, respectively. The effectiveness of the suggested LOS guidance system is assured through computer simulation.

This paper describes the realization of a coordinates tracking algorithm for an EOTS (Electro-Optical Tracking System). The EOTS stabilizes the image sensors, tracks targets automatically, and provides navigation capability for vehicles. The coordinates tracking algorithm calculates the azimuth and the elevation angle of an EOTS using the inertial navigation system and the attitude sensors of the vehicle, so that LOS designates the target coordinates which are generated by a Radar. In the error analysis, the unexpected behaviors of an EOTS due to the time delay and deadbeat of the digital signals of the vehicle equipments are anticipated and the countermeasures are suggested. The application of this algorithm to an EOTS will improve the operational capability by reducing the time which is required to find the target and support flight especially in the night time flight and the poor weather condition.

This paper proposes a theoretically rigorous analysis procedure that compares the position domain and range domain carrier-smoothed-code filters for differential GNSS positioning. Utilizing consistent error covariance formulation, it is shown that filtering in the position domain is, in theory, more advantageous than range domain carrier-smoothed-code filtering. It is also shown that if the visible satellite set does not change during a sufficiently long time interval the performances of position and range domain filters are similar.

As the Internet real-time multimedia applications increases, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate that is based on the available round trip time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used in the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.