• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1504-1507
• /
• 1997

In this paper, we investigate a traffic flow modeled by stochastic Petri nets. The model consists of two parts : the traffic flow model and signal controller model. These models are used for analyzing the flow of the traffic intersection. The results of the evaluation are derived from a Petri Net-based simulation package, Greatspn. Through simulation we compare the performances of the pretimed signal controller with those of the trafic-adaptive signal controller.

• Proceedings of the IEEK Conference
• /
• 2001.06a
• /
• pp.41-44
• /
• 2001

In this paper, we have investigated the adaptive sector antenna which can control the size and direction angle of sectors, and proposed the three sectorization algorithms which are based on distribution of users, distribution of codes and distribution of signal power, respectively. The BERs of each sectorization algorithms are compared through computer simulation. As results of the simulation, the sectorization algorithm based on signal power are better than the other in terms nf BER. We have simulated error performance the DS-CDMA/BPSK system with the antenna in AWGN, frequency non-selective Rayleigh fading and MAI channels.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.177-180
• /
• 1996

The purpose of this paper is to design a robust adaptive control algorithm for a class of systems having continuous relay nonlinearity. This continuous relay nonlinearity can be defined as an analytic nonlinear function having unknown parameters and bounded unmodeling part. By this mathematical modeling, the whole system can be considered as a nonlinear system having unknown parameters and bounded perturbation. The control algorithm of this paper, RALC, can be constructed by robust adaptive law, feedback linearization, and indirect robust adaptive control. By this RALC, we can obtain that the output of given system can follow that of a stable reference linear model made by designer and the boundedness of all signals in closed-loop system can be maintained. Therefore, we can confirm a robust adaptive control for a class of systems having continuous relay nonlinearity.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10b
• /
• pp.7-12
• /
• 1993

This paper presents a generalized truncated least, squares adaptive algorithm and a two-stage design method. The proposed algorithm is directly derived from the normal equation of the generalized truncated least squares method (GTLSM). The special case of the GTLSM, the truncated least squares (TLS) adaptive algorithm, has a distinct features which includes the case of minimum steps estimator. This algorithm seemed to be best in the deterministic case. For real applications in the presence of disturbances, the GTLS adaptive algorithm is more effective. The two-stage design method proposed here combines the adaptive control system design with a conventional control design method and each can be treated independently. Using this method, the validity of the presented algorithms are examined by the simulation studies of an indirect adaptive control.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.5
• /
• pp.977-983
• /
• 2011

This paper proposes a new adaptive speed controller to achieve a robust speed control of a permanent magnet synchronous motor(PMSM). The proposed adaptive regulator does not require any information on the motor parameter and load torque values, so it is very insensitive to model parameter and load torque variations. Also, the stability of the proposed adaptive control system is proven. To validate the robustness of the proposed adaptive speed controller, both simulation and experimental results are provided under motor parameter and load torque variations. It is clearly demonstrated that the proposed adaptive regulator can accurately control the speed of permanent magnet synchronous motors.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.101-106
• /
• 1997

This paper proposes a stable adaptive neural network control(NNC) for fixable joint manipulators. For designing the stable adaptive NNC, the flexible system dynamics is separated into fast and slow subdynamics according to singular perturbation concept. For the slow subdynamics, an adaptive NNC is designed to warrant the system stability and NN learning by lyapunov stability criterion. And to stabilize the fast dynamics, derivative control loop is installed. Through numerical simulation, the performance of the proposed NNC was compared to that of an adaptive controller designed based on the knowledge of the system dynamics. The proposed NNC shows much improvement over the conventional adaptive controller.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.04a
• /
• pp.305-311
• /
• 2003

We propose a new technique of adaptive-neuro controller design to implement real-time control of robot manipulator, Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of loaming a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real time control of robot system using DSPs(TMS320C50)

• International Journal of Control, Automation, and Systems
• /
• v.1 no.4
• /
• pp.444-452
• /
• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

• Journal of Navigation and Port Research
• /
• v.30 no.2
• /
• pp.119-124
• /
• 2006

In Part I(theoretical study) of the paper, a new adaptive autopilot for ships based on Adaptive Neural Networks was proposed. The ANNAI autopilot was designed for course-keeping, turning and track-keeping control for ships. In this part of the paper, to show the effectiveness and feasibility of the ANNAI autopilot and automatic selection algorithm for learning rate and number of iterations, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented. Additionally, the results of the previous studies using Adaptive Neural Network by backpropagation algorithm are also showed for comparison.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.73-85
• /
• 2001

The spatial population existing in a plane ares, such as an animal or aerial population, have certain relationships among regions which are located within a fixed distance from one selected region. We consider with the adaptive searching estimation in spatial sampling for a spatial population. The adaptive searching estimation depends on values of sample points during the survey and on the nature of the surfaces under investigation. In this paper we study the estimation by the adaptive searching in a spatial sampling for the purpose of estimating the area possessing a particular characteristic in a spatial population. From the viewpoint of adaptive searching, we empirically compare systematic sampling with stratified sampling in spatial sampling through the simulation data.