There are mainly two types of bang-bang controllers for nominal linear time-invariant (LTI) system. Optimal bang-bang controller is designed based on optimal control theory and suboptimal bang-bang controller is obtained by using Lyapunov stability condition. In this paper, the suboptimal bang-bang control method is extended to LTI system involving both control input saturation and structured real parameter uncertainties by using Lyapunov robust stability condition. Two robust optimal bang-bang controllers are derived by minimizing the time derivative of Lyapunov function subjected to the limit of control input. The one is developed based on the classical quadratic stability(QS), and the other is developed based on the affine quadratic stability(AQS). And characteristics of the two controllers are compared. Especially, bounds of parameter uncertainties which theoretically guarantee robust stability of the two controllers are compared quantitatively for 1DOF vibrating system. Moreover, the validity of robust optimal bang-bang controller based on the AQS is shown through numerical simulations for this system.

In this study, a thermal control system which applied a Peltier device for the polymerase chain reaction(PCR) machine is to be designed. Here in order for it to easily follow the characteristics of the thermal cycle existing for gene amplification of the PCR sample, a PCR control board utilizing a thermal sensor, a Peltier, and a 8 bit microprocessor is made up. Especially a fuzzy type PD control algorithm is applied periodically in time response, and control system is implemented. For that matter, the characteristic data of subject system is obtained and analysed to begin with. Based on this analysed data, the proposed control algorithm is applied and an evaluation of the performance of the whole system take place through the PC.

To meet an increasing demand for high performance in gun dynamic plant, both a precise and a fast response positioning are strongly required for the gun servo system. A mode switching control(MSC) scheme, which includes a fine stabilizing controller, fast positioning one and a switching function, is widely used to meet this requirement. Stabilization is performed through PID controller, while a time optimal control method is used for target designation. In this paper, a modified PTOS(Proximate Time Optimal Servomechanism) algorithm is derived so as to accommodate the damping term in the gun plant model. Also, applying a mode switching strategy, the bumpless transfer is made possible when the controller switches from PTOS to PID. To show the effectiveness of the overall control system, simulation results are given including the gun dynamics.

In this paper vector code correlation(VCC) method and an algorithm to promote the image-processing performance in building an effective measurement system using cameras are described far automatically berthing and controlling the ship equipped with side-thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built-in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image-processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image-processing time of fourfold as compared with the typical template matching method.

In this paper, We had proposed SDR module, and designed FPGA to compose with channel separation of broadband CDMA signal what have multiple FA. At decomposition and composition process of multiple FA CDMA signal, system only progress decomposition and composition of channel selected by software. Therefore, proposed system can manage base station transceiver system very effectively than the other way what send on all band of multiple CDMA signal. Also, it is possible that system sets again coefficient of each filter because it is consisted of SDR module. Therefore, we can easily control coefficient each filter according to base station transceiver system environment.

Path planning is a key element in navigation of a mobile robot. Several algorithms such as a gradient method have been successfully implemented so for. Although the gradient method can provide the global optimal path, it computes the navigation function over the whole environment at all times, which result in high computational cost. This paper proposes a high-speed path planning scheme, called a gradient method with topological information, in which the search space for computation of a navigation function can be remarkably reduced by exploiting the characteristics of the topological information reflecting the topology of the navigation path. The computing time of the gradient method with topological information can therefore be significantly decreased without losing the global optimality. This reduced path update period allows the mobile robot to find a collision-free path even in the dynamic environment.

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Although MCL is capable of estimating the robot pose even for a completely unknown initial pose in the known environment, it takes considerable time to give an initial pose estimate because the number of random samples is usually very large especially for a large-scale environment. For practical implementation of MCL, therefore, a reduction in sample size is desirable. This paper presents a novel approach to reducing the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information generated through the thinning technique, which is commonly used in image processing, is employed. The global topological map is first created from the given grid map for the environment. The robot then scans the local environment using a laser rangefinder and generates a local topological map. The robot then navigates only on this local topological edge, which is likely to be similar to the one obtained off-line from the given grid map. Random samples are drawn near the topological edge instead of being taken with uniform distribution all over the environment, since the robot traverses along the edge. Experimental results using the proposed method show that the number of samples can be reduced considerably, and the time required for robot pose estimation can also be substantially decreased without adverse effects on the performance of MCL.

Recently proposed control scheme for a stable teleoperation, which was based on two-port time-domain passivity approach[21], has been successful for a contact with high stiffness environments. However, we found several conservatisms during the contact with deformable environments and unconstrained motion. The two-port time-domain passivity controller was excessively dissipating energy even though it was not necessary for some cases of an unconstrained motion and soft contact. The main reason of those conservatisms was on the fact that the two-port time-domain passivity controller was activated without considering the amount of energy dissipation at the master and slave manipulators. Especially, the exclusion of the slave manipulator from the two-port was the dominant reason of the conservatisms. In this paper, we consider the amount of energy dissipation at slave manipulator for designing the time-domain passivity observer and controller. The measured interaction force between slave manipulator and environment allow the time-domain passivity observer to include the amount of energy dissipation at the slave manipulator. Based on the modified passivity observer, reference energy following method[24] is applied to satisfy the passivity condition in real-time. The feasibility of the developed methods is proved with experiments. Improved performance is obtained for an interaction with deformable environments and an unconstrained motion.

This paper presents the two degree-of-freedom (DOF) planar parallel mechanism, called the $2{\underline{R}}RR-RP$ manipulator, whose degree-of-freedom is dependent on an additional passive constraining leg connecting the base and the platform. First, the kinematic analysis of the mechanism is performed: the inverse and forward kinematic problems are analytically solved, the workspace is systematically derived, and all of the singular configurations are examined. Then, in order to determine the geometric parameters the optimization of the mechanism is performed considering its dexterity, stiffness, and space utilization. Finally, the kinematic performances of the optimized mechanism are evaluated through the comparison study to the conventional 5-bar parallel manipulator.

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

Industrial and economical importance of CP(Cellular Phone) is growing rapidly. Combined with IT technology, CP is one of the most attractive technologies of today. However, unless we find a new breakthrough in the technology, its growth may slow down soon. RT(Robot Technology) is considered one of the most promising next generation technologies. Unlike the industrial robot of the past, today's robots require advanced features, such as soft computing, human-friendly interface, interaction technique, speech recognition object recognition, among many others. In this paper, we present a new technological concept named RCP (Robotic Cellular Phone) which integrates RT and CP in the vision of opening a combined advancement of CP, IT, and RT, RCP consists of 3 sub-modules. They are $RCP^{Mobility}$(RCP Mobility System), $RCP^{Interaction}$, and $RCP^{Integration}$. The main focus of this paper is on $RCP^{Mobility}$ which combines an autonomous navigation system of the RT mobility with CP. Through $RCP^{Mobility}$, we are able to provide CP with robotic functions such as auto-charging and real-world robotic entertainment. Ultimately, CP may become a robotic pet to the human beings. $RCP^{Mobility}$ consists of various controllers. Two of the main controllers are trajectory controller and self-localization controller. While the former is responsible for the wheel-based navigation of RCP, the latter provides localization information of the moving RCP With the coordinates acquired from RFID-based self-localization controller, trajectory controller refines RCP's movement to achieve better navigation. In this paper, a prototype of $RCP^{Mobility}$ is presented. We describe overall structure of the system and provide experimental results on the RCP navigation.

The random-based cleaning algorithm is a simple algorithm widely used in commercial vacuum cleaning robots. This algorithm has two limitations, that is, cleaning takes a long time and there is no guarantee that the cleaning will cover the whole cleaning area. This has lead to customer dissatisfaction. Thus, in recent years, many intelligent cleaning algorithms that takes into consideration information gathered from the cleaning area environment have been proposed. The plowing-based algorithm, which is the most efficient algorithm known to date when there are no obstacles in the cleaning area, has a deficiency that when obstacle prevail, its performance is not guaranteed. In this paper, we propose the Group-k algorithm that is efficient for that situation, that is, when obstacle prevail. The goal is not to complete the cleaning as soon as possible, but to clean the majority of the cleaning area as fast as possible. The motivation behind this is that areas close to obstacles are usually difficult for robots to handle, and hence, many require human assistance anyway In our approach, obstacles are grouped by the complexity of the obstacles, which we refer to as 'complex rank', and then decide the cleaning route based on this complex rank. Results from our simulation-based experiments show that although the cleaning completion time takes longer than the plowing-based algorithm, the Group-k algorithm cleans the majority of the cleaning area faster than the plowing algorithm.

This paper presents a new technique that derives the dynamic acceleration bounds of multiple cooperating robot systems from given individual torque limits of robots. A set of linear algebraic homogeneous equation is derived from the dynamic equations of multiple robots with friction contacts. The mobility of the robot system is analyzed by the decomposition of the null space of the linear algebraic equation. The acceleration bounds of multiple robot systems are obtained from the joint torque constraints of robots by the medium of the decomposed null space. As the joint constraints of the robots are given in the infinite norm sense, the resultant acceleration bounds of the systems are described as polytopes. Several case studies are presented to validate the proposed method in this paper.

This paper presents a new analytical approach for designing multiloop PI controllers for disturbance rejection in multivariable processes with time delay. The proposed method is based on IMC-PID design approach. To overcome a sluggish load response by dominant pole in the process, the IMC filter is modified to compensate the dominant pole effect. Based on the modified IMC filter, an analytical tuning rule for multiloop PI controller is driven by extending the generalized IMC-PID method for single input/single output (SISO) systems [1] to multi input/multi output (MIMO) systems. Simulation results show that the proposed method gives a satisfactory load performance as well as servo performance in the multiloop system.

This paper presents a navigation error compensation scheme for Strap-Down Inertial Navigation System(SDINS) using electro-optical sensor. The proposed scheme uses the position or the attitude information from the sensor. For each case, Kalman filter model is derived and implemented. To show the effectiveness of the present compensation scheme, computer simulations have been carried out resulting in the boundedness of position and attitude errors.