• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.428-437
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• 2006

Many physical systems are hybrid in the sense that they have continuous behaviors and discrete phenomena. In control system with multiple models, switching strategy and stability of the closed-loop system under switching are very important issues. In this paper, a novel adaptive control scheme based on multiple parameter models is proposed to cope with a change in Parameters. Switching strategy guarantees the non-increase in the global control Lyapunov function if the estimation of Lyapunov function value converges. Least-square estimation is used to find the estimated value of the Lyapunov function. Switching and adaptation law guarantees the stability of closed-loop system in the sense of Lyapunov. Simulation results on anti-lock brake system are shown to verify the effectiveness of the proposed controller in view of a large change in system parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1149-1154
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• 1991

For a robotic workcell, which consists of multiple robots, several interconnection methods are presented in terms of the processor based architecture. Since few attempts have been made to formulate and analyze multiple robot system(MRS), we turn the knowledge of multiple processor system(MPS) or multiple computer system(MCS) to good account. The performance evaluation is achieved through queueing analysis, the aim being to compare their response time, utilization, probability of service failure under different workload. To verify the validity of the proposed analysis methods, a computer simulation is performed. The results together with comments presented here give some useful guidelines for the selection of an appropriate interconnection method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.201-204
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• 1998

This paper presents a new construction method of candidate controllers using Multi-modal Neural Network(MNN). To improve a control performance of multiple controller, we construct, candidate controllers which consist of MNN. MNN can learn more complicated function than multilayer neural network. MNN consists of preprocessing module and neural network module. The preprocessing module transforms input signals into spectra which are used as input of the following neural network module. We apply the proposed method to multiple control system which controls the cart-pole balancing system and show the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.147-152
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• 2016

This paper proposes a new TPC (Transmission Priority Change) algorithm which is used to diagnose failures of a CAN (Controller Area Network) based network system for the oil tank monitoring. The TPC algorithm is aimed to increase the total amount of data transmission and to minimize the latency for an urgent message by changing transmission priority. The urgency of the data transmission has been determined by the conditions of sensors. There are multiple sensors inside of the oil tank, such as temperature, valve, pressure and level sensors. When the sensors operate normally, the sensory data can be collected through the CAN network by the monitoring system. However when there is a dangerous situation or failure situation happened at a sensor, the data need to be handled quickly by the monitoring system, which is implemented by using the TPC algorithm. The effectiveness of the TPC algorithm has been verified by the real experiments. In addition, this paper introduces a method that people can figure out the condition of oil tanks and also can perform the fault diagnosis in real-time by using transmitted packet data. By applying this TPC algorithm to various industries, the convenience and reliability of multiple sensors network system can be improved.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.4
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• pp.381-388
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• 1997

This paper proposes a heuristic algorithm to efficiently perform line balancing in the PCB assembly line including multiple surface mounters efficiently. Generally, the problems in line balancing are classified into two kinds. Firstly, is the determining of the minimum number of machines required for achieving the desired production rate. Secondly, is the assign of jobs to multiple machines in order to minimize the cycle time which is defined as a maximum among the working times of machines when the number of machines is fixed. In this paper, we deal with the latter. We consider a PCB assembly line, including the multiple surface mounters arranged serially as a target system. Also, the conveyor is assumed to move at a constant speed and have no buffer. Considering that the minimum number of machines required for the desired production rate is a discrete nonincreasing function which is inversely proportional to the cycle time, we propose an optimization algorithm for line balancing by using the binary search method. The algorithm is validated through computer simulation, the results of which show that their shapes coincide nearly with those of optimal line balancing efficiency graphs regardless of the number of components, the performance of surface mounters, and the structure of assembly line.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.741-747
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• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.37.2-37
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• 2002

1. Introduction 2. Problem Formulation 3. Avoidability Measure And Relative Distance 4. Artificial Potential Field in Terms of Relative Distance 5. Simulation Results 6. Conclusions 7. Reference

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.106.3-106
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• 2002

1. Introduction 2. Petri-Net 3. Dynamic environment recognition 4. Simulation 5. Conclusion. References

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.488-492
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• 1991

This paper deals with modeling and scheduling for flexible manufacturing systems(FMS). In modeling extended Petri nets are used to describe various and complex properties. To resolve multiple conflict without predefined properties, such as part selection, machine selection, rule-based scheduling method is introduced. This structure have a capability of on-line scheduling with systematic modeling description.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.5-41
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• 2001

A novel method is introduced for determining the weights of a regularization network which approximates one-to-many mapping. A conventional neural network will converges to the average value when outputs are multiple for one input. The capability of proposed network is demonstrated by an example of learning inverse mapping.