• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.596-602
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• 2007

Recently, many ECUs(Electronic Control Units) have been used to enhance the vehicle safety, which leads to a distributed real-time control system. The distributed real-time control system requires to reduce the network delay for dependable real-time performance. There are two different paradigms by which a network protocol operates: event-triggered and time-triggered. This paper focuses on implementation of a time-triggered protocol. i.e. TTP/C(Time-Triggered Protocol/class C). This paper presents a design method of TTP control network and performance evaluation of distributed real-time control system using TTP protocol.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.39-41
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• 1995

The inherent performance of Ultrasonic Motor(USM) which is on of highlighted a directly-driven positioning servo motor/actuator. In this paper, the speed of control USM based on neural network control. The neural network control can roughly be classified as the direct control and indirect control schemes. An indirect control scheme is adopted for Ultrasonic Motor speed control. A back propagation algorithm is used to train neural network controller. The Simulation results show that this neural network control system can provide good dynamical responses.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.186-193
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• 2008

The Internet's low cost and ubiquity present an attractive option for real-time distributed control of processes on the factory floor. When integrated with the Internet, the LonWorks open control network can give ubiquitous accessibility with the distributed control nature of information on the factory floor. One of the most important points in real-time distributed control of processes is timely response. There are many processes on the factory floor that require timely response. However, the uncertain time delay inherent in the network makes it difficult to guarantee timely response in many cases. Especially, the transmission characteristics of the LonWorks/IP network show a highly stochastic nature. Therefore, the time delay problem has to be resolved to achieve high performance and quality of the real-time distributed control of the process in the LonWorks/IP Virtual Device Network (VDN). It should be properly predicted and compensated. In this paper, a new distributed control scheme that can compensate for the effects of the time delay in the network is proposed. It is based on the PID controller augmented with the Smith predictor and disturbance observer. Designing methods for output feedback filter and disturbance observer are also proposed. Tracking position control experiment of a geared DC Servo motor is performed using the proposed control method. The performance of the proposed controller is compared with that of the Internal Model Controller (IMC) with the Smith predictor. The result shows that the performance is improved and guaranteed by augmenting a PID controller with both the Smith predictor and disturbance observer under the stochastic time delay in the LonWorks/IP VDN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.495-502
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• 2013

In the mobile Ad hoc Network(MANET), improving technique for management and control of topology is recognized as an important part of the next generation network. In this paper, we proposed an efficient node life time management of ATICC(Adaptive Time Interval Clustering Control) in Ad-hoc Networks. Ad-hoc Network is a self-configuration network or wireless multi-hop network based on inference topology. This is a method of path routing management node for increasing the network life time through the periodical route alternation. The proposed ATICC algorithm is time interval control technique depended on the use of the battery energy while node management considering the attribute of node and network routing. This can reduce the network traffic of nodes consume energy cost effectively. As a result, it could be improving the network life time by using timing control method in ad-hoc networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.192-202
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• 2004

In this paper we have proposed policy-based network management system architecture for Internet QoS support Mobile IP networks that is divided into four layers(application layer, information management layer, policy control layer, device layer), then we propose an implementation strategy of policy-based network management system to enforce various control and network management operations and a model of policy server using SCOPS(Simple Common Open Policy Service) protocol that is developed in this research. For policy-based mobile IP network management system implementation, we have derived four policy classes(access control, mobile IP operation, QoS control, and network monitoring) and we showed operation procedures for each policy scenarios. Finally we have implemented Internet QoS support policy-based mobile IP network testbed and management system and verified out DiffServ policy enforcement behaviors for a target class service that is arranged a specific bandwidth on network congestion conditions.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.39-46
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• 1998

In this paper, we present a neural network identification and a control of highly complicated nonlinear left ventricular assist device(LVAD) system with a pneumatically driven mock circulation system. Generally, the LVAD system needs to compensate for nonlinearities. It is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with neural network identification(NNI). Once the NNI has learned the dynamic model of the LVAD system, the other network, called neural network controller(NNC), is designed for a control of the LVAD system. The ability and effectiveness of identifying and controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• Journal of Power Electronics
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• v.11 no.3
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• pp.279-284
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• 2011

Network-based controls for ac motor drive systems are becoming increasingly important. In this paper, an ac motor control system is implemented by a motor control module and three sensor modules such as a voltage sensor module, a current sensor module, and an encoder module. There will inevitably be network time delays from the sensor modules to the motor control system, which often degrades and even destabilizes the motor drive system. As a result, it becomes very difficult to estimate the network delayed ac sensor data. An algorithm to reduce the effects of network time delays on sensor data is proposed, using both a synchronization signal and a simple method for estimating the sensor data. The algorithm is applied to a vector controlled induction motor drive system, and the performance of the proposed algorithm is verified with experiments.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.282-284
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error. Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Speed controller of an induction motor based fuzzy-neural network is proposed and the speed control of an induction motor is performed using the proposed controller. Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.179-179
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• 2000

An adaptive neural network compensator for stick-slip friction phenomena in servo systems is proposed to supplement the traditionally available position and velocity control loops for precise motion control. The neural network compensator plays a role of canceling the effect of nonlinear slipping friction force. This enables the mechatronic systems more precise control and realistic design in the digital computer. It was confirmed that the control accuracy is more improved near zero velocity and the points of changing the moving direction through numerical simulation