This study explores the course tracking control problem of unmanned surface vessels (USVs) under the influence of actuator faults and internal and external uncertainties. In the control strategy desig n, we first model the unknown dynamics and use adaptive technology to construct an online appro ximator to compensate for the unknown dynamics of the system. Under the framework of adaptive backstepping, a robust adaptive course tracking control scheme is constructed. This control strategy does not require any prior knowledge of the model in advance. The stability analysis of the theoret ical mathematical derivation of the control strategy was conducted based on Lyapunov stability theo ry. Finally, the effectiveness of the control strategy proposed in this paper was verified through sim ulation.

This research introduced a command-filtered backstepping control of mirror system to maintain laser communication between satellite and ground station. This requires a 2 degree of freedom gimbal mirror system using DC motors for target acquisition, pointing, and tracking (APT) system. This APT system is used for laser communication between satellite and ground stations. To track these desired angles, we have to control DC motors using introduced command-filtered backstepping controller (CFBSC) with disturbance. Command filtered backstepping controller has second order filter instead differentiation for simple and fast calculation. Introduced command-filtered backstepping control gives a smooth control signal for intermediate states. Simulation results verify that CFBSC outperforms SMC in terms of tracking error and disturbance rejection.

This paper describes the design of H-infinity controller for robust control of a DC motor system. The suggested controller can ensure robustness against disturbance and model uncertainty by minimizing H-infinity norm of the transfer function from exogenous input to performance output and applying the small gain theorem. In particular, the controller was designed to reduce the effects of disturbance and model uncertainty simultaneously by formalizing these problems as a mixed sensitivity problem. The validity of the proposed controller was demonstrated by computer simulations and real experiments. Moreover, the effectiveness of the proposed controller was confirmed by comparing its performance with PI controller, which was tested under the same experimental condition as the H-infinity controller.

A robust adaptive control approach is proposed for underactuated surface ship linear path-tracking control system based on the backstepping control method and Lyapunov stability theory. By employing T-S fuzzy system to approximate nonlinear uncertainties of the control system, the proposed scheme is developed by combining “dynamic surface control” (DSC) and “minimal learning parameter” (MLP) techniques. The substantial problems of “explosion of complexity” and “dimension curse” existed in the traditional backstepping technique are circumvented, and it is convenient to implement in applications. In addition, an auxiliary system is developed to deal with the effect of input saturation constraints. The control algorithm avoids the singularity problem of controller and guarantees the stability of the closed-loop system. The tracking error converges to an arbitrarily small neighborhood. Finally, MATLAB simulation results are given from an application case of Dalian Maritime University training ship to demonstrate the effectiveness of the proposed scheme.

Control Chart is a graph which dots the characteristic values of a process. It is the tool of statistical technique to keep a process in controlled condition. It is also used for investigating the state of a process. Therefore many companies have used Control Chart as the tool of statistical process control (SPC). Products from a production process represent accidental dispersion values around a certain reference value. Fluctuations cause of quality dispersion is classified as a chance cause and a assignable cause. Chance cause refers unmanageable practical cause such as operator proficiency differences, differences in work environment, etc. Assignable cause refers manageable cause which is possible to take actions to remove such as operator inattention, error of production equipment, etc. Traditionally x-R control chart or x-s control chart is used to find and remove the error cause. Traditional control chart is to determine whether the measured data are in control or not, and lets us to take action. On the other hand, RNELCC (Reflected Normal Expected Loss Control Chart) is a control chart which, even in controlled state, indicates the information of economic loss if a product is in inconsistent state with process target value. However, contaminated process can cause control line sensitive and cause problems with the detection capabilities of chart. Many studies on robust estimation using trimmed parameters have been conducted. We suggest robust RNELCC which used the idea of trimmed parameters with RNEL control chart. And we demonstrate effectiveness of new control chart by comparing with ARL value among traditional control chart, RNELCC and robust RNELCC.

폴리벤즈이미다졸(PBI)는 현재 상용 고분자들 중에서 가장 내열성이 좋은 이종고리화합물이다. 우수한 기계적, 화학적 물성 때문에 해당 고분자는 나노공학, 전기공학, 광학, 재료공학과 같은 분야 외에도 쓰임새가 다양하다. 본 연구는 다 공성 폴리벤즈이미다졸 분리막 제조에 있어서 다양한 조건들을 변화함에 따라 나타나는 모폴로지를 관찰하고자 하였고, 그 결과를 이용하여 나노여과막으로써의 다양한 모폴로지 조절을 용이하게 하고자 하였다. 폴리벤즈이미다졸 분리막 제조는 용매-비용매 상전이법을 이용하였고 나이프캐스팅 법을 통하여 분리막 을 제조하였다. 용매와 조용매는 각각 DMAc와 THF를 사용하였으며 IPA와 증류 수로 세척하였다. 모폴로지의 관찰은 주사전자현미경(SEM)을 통해 진행 되었으며 표면과 단면 위주로 촬영을 진행하였다.

Recently, the production cycle in manufacturing process has been getting shorter and different types of product have been produced in the same process line. In this case, the control chart using coefficient of variation would be applicable to the process. The theory that random variables are located in the three times distance of the deviation from mean value is applicable to the control chart that monitor the process in the manufacturing line, when the data of process are changed by the type of normal distribution. It is possible to apply to the control chart of coefficient of variation too.  ,  estimates that taken in the coefficient of variation have just used all of the data, but the upper control limit, center line and lower control limit have been settled by the effect of abnormal values, so this control chart could be in trouble of detection ability of the assignable value. The purpose of this study was to present the robust control chart than coefficient of variation control chart in the normal process. To perform this research, the location parameter, xα, sα were used. The robust control chart was named Tim-CV control chart. The result of simulation were summarized as follows; First, P values, the probability to get away from control limit, in Trim-CV control chart were larger than CV control chart in the normal process. Second, ARL values, average run length, in Trim-CV control chart were smaller than CV control chart in the normal process. Particularly, the difference of performance of two control charts was so sure when the change of the process was getting to bigger. Therefore, the Trim-CV control chart proposed in this paper would be more efficient tool than CV control chart in small quantity batch production.

Control charts are generally used for process control, but the role of traditional control charts have been limited in case of a non-contaminated process. Traditional x-s control chart has not been activated well for such a problem because of trying to control processes as center line and control limits changed by the contaminated value. This paper suggests modified x-s control chart based on robust estimation. In this paper, we consider the trimmed mean of the sample means and the trimmed mean of the sample standard deviations. By comparing with ARL value, the responding results are decided. The comparison resultant results of traditional control chart and modified control chart are contrasted.

Control charts are generally used for process control, but the role of traditional control charts have been limited in case of a contaminated process. Traditional x control charts have not been activated well for such a problem because of trying to control processes as center line and control limits changed by the contaminated value. This paper is to propose robust x control charts which is considering a location parameter in order to respond to contaminated process. In this paper, we consider x, that is trimmed rate; typically ten percent rate is used. By comparing with p, ARL value, the responding results are decided. The comparison resultant results of proposed two control charts are shown and are well contrasted.

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is in

Monitoring autocorrelated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment throug

건물의 능동 진동 제어에 있어서 제어기의 제어입력의 포화와 건물의 파라미터 불확실성을 동시에 고려하는 제어 방법이 필요하다. 저자들의 이전 논문에서는 제어 입력에 포화가 존재하는 불확실한 선형 시불변계에 대하여 강인 안정성과 제어 성능이 보장되는 강인 포화 제어기를 제안하였다. 본 논문에서는 능동 질량 감쇠기 (AMD)가 설치된 건물의 능동 진동 제어에 대한 제안된 강인 포화 제어기의 유용성을 실험적으로 검증한다. 실험은 유압식 AMD가 설치된 2층의 건물 모형에 대하여 수행된다.

Statistical process cintrol is intended to assist operators of a stable system in monitoring whether a change has occurred in the process, and it uses several control charts as main tools. In design and use of control chart, it is rational that probabilit

In this study, we establish bootstrap control limits for robust M-estimator chart by applying the bootstrap method, called resampling, which could not demand assumptions about pre-distribution when the process is skewed and/or the normality assumption is doubt. The results obtained in this study are summarized as follows : bootstrap M-estimator control chart is developed for applying bootstrap method to M-estimator chart, which is more robust to keep Type Ⅰ error when process contain contaminate quality characteristic.

구조물의 능동제어 시스템에서 제어기 설계에 사용되는 구조계의 모델과 실구조계의 차이는 시스템의 성능저하 및 불안정성을 유발할 수 있다 이연구에서는 무시된 고차모우드와 같이 주파수영역에서 표현되는 비구조적 불확실성에 대하여 시스템의 안정성을 보장하도록 강인성을 가지는 LQG/LTR제어이론을 사용하여 구조물의 지진응답제어에 효과적으로 사용할 수 있는 제어기 설계방법을 제시한다 특히 고층건물이나 교탑과 같은 구조물의 지진응답 제어에 적용할 수 있도록 각층의 절대 가속도를 측정변수로 층간상대변위를 제어변수로 설정하여 최적제어기를 구성한다 El Centro 지진압력을 받는 6자유도 전단빌딩모델에 대하여 제어기를 설계하거 수치모사를 수행하여 제시한 제어기가 안정도-강인성을 가지고 지진응답제어에 효과적임을 보인다.

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.

Unlike normal wheels, the Mecanum wheel enables omni-directional movement regardless of the orientation of a mobile robot. In this paper, a robust trajectory tracking control method is developed based on the dynamic model of the Mecanum wheel mobile robot in order that the mobile robot can move along the given path in the environment with disturbance. The method is designed using the impedance control to make the mobile robot to track the path, and the integral sliding mode control for robustness to disturbance. The good performance of the proposed method is verified using the MATLAB /Simulink simulation and also through the experiment on an actual Mecanum wheel mobile robot. In both the simulation and the experimentation, we make the mobile robot move along a reference trajectory while maintaining the robot's orientation at a constant angle to see the characteristics of the Mecanum wheel.

An electric motor is the one of the most important parts in robot systems, which mainly drives the wheel of mobile robots or the joint of manipulators. According to the requirement of motor performance, the controller type and parameters vary. For the wheel driving motors, a speed tracking controller is used, while a position tracking controller is required for the joint driving motors. Moreover, if the mechanical parameters are changed or a different motor is used, we might have to tune again the controller parameters. However, for the beginners who are not familiar about the controller design, it is hard to design pertinently. In this paper, we develop a nominal robust controller model for the velocity tracking of wheel driving motors and the position tracking of joint driving motors based on the disturbance observer (DOB) which can reject disturbances, modeling errors, and dynamic parameter variations, and propose the methodology for the determining the least control parameters. The proposed control system enables the beginners to easily construct a controller for the newly designed robot system. The purpose of this paper is not to develop a new controller theory, but to increase the user-friendliness. Finally, simulation and experimental verification have performed through the actual wheel and joint driving motors.

This paper presents a robust lane detection algorithm based on RGB color and shape information during autonomous car control in realtime. For realtime control, our algorithm increases its processing speed by employing minimal elements. Our algorithm extracts yellow and white pixels by computing the average and standard deviation values calculated from specific regions, and constructs elements based on the extracted pixels. By clustering elements, our algorithm finds the yellow center and white stop lanes on the road. Our algorithm is insensitive to the environment change and its processing speed is realtime-executable. Experimental results demonstrate the feasibility of our algorithm.