• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.61-64
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS32OC50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator. In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters are determined by the pole-placement method. Performance of self-tuning adaptive controller is illustrated by the simulation and experiment for a SCARA robot.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1345-1355
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• 2004

In a sender-initiated load redistribution algorithm, a sender(overloaded processor) continues to send unnecessary request messages for load transfer until a receiver(underloaded processor) is found while the system load is heavy. In a receiver-initiated load redistribution algorithm, a receiver continues to send unnecessary request messages for load acquisition until a sender is found while the system load is light. Therefore, it yields many problems such as low CPU utilization and system throughput because of inefficient inter-processor communications in this environment. This paper presents an approach based on genetic algorithm(GA) for adaptive load sharing in distributed systems. In this scheme, the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.239-240
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• 2007

In this paper, a robust adaptive backstepping controller will be proposed for the speed control of permanent magnet synchronous motors in using electrical vehicles. Stator resistance, damping coefficient, load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the Proposed controller.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.487-497
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• 2010

In this paper, we propose a hard handover scheme which adaptively decides important handover parameters such as hysteresis and time-to-trigger values considering the load difference between the target and serving cells. First of all, the hysteresis value can be automatically adjusted according to the load difference, thus it is used to decide the handover trigger time. As a result, through the adaptive hysteresis scheme, handover drop rate is improved. However, this adaptive hysteresis scheme has a problem that the ping-pong effect, which occurs due to the frequent movement of mobile stations at the cell boundary, is increased. Therefore, to solve this problem, we propose a novel adaptive time-to-trigger scheme with the time-to-trigger which is in inverse proportion to the hysteresis value already established by the adaptive hysteresis scheme which adapts to the changing load difference between the target and serving cells. The simulation results show that the proposed adaptive time-to-trigger scheme based on the adaptive hysteresis is better than existing schemes in terms of handover drop rate and ping-pong generation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.764-774
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• 2013

The number of users and data packets has increased in 4G cellular networks. Therefore, 4G cellular network providers suffer from the network capacity problem. In order to solve this problem, femtocell concept is suggested. It can reduce the coverage hole and enhance the QoS. However, only small number of femtocells experience the large amount of loads. To solve this problem, Mobility Load Balancing (MLB) algorithm is suggested, which is a kind of load balancing algorithm. To distribute the traffic load, MLB algorithm modifies the handover region. If the handover region is reduced by MLB algorithm, some cell edge users are compulsively handed over to neighbor femtocell. In this paper, we analyze the relation between MLB performing period and performance indicators. For example throughput and blocking probability is reduced, if period is decreased. On the contrast, if period is increased, the number of handover frequency is decreased. Using this relation, we suggest the adaptive periodic MLB algorithm. This algorithm includes the advantage of both long period and short period MLB algorithm, such as high throughput, the small number of handover frequency, and low blocking probability.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.321-325
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• 1988

The objective of this study is to develop a microcomputer-based adaptive controller for an electro hydraulic velocity control system subjected to the variation of system parameters. The step response performance of the system with the adaptive controller is investigated for the variation of the external load torque, the moment of inertia and the reference inputs, and compared with that obtained by PID controller whose gains are constant. The experimental results show that this proposed model reference adaptive controller is robust to the variation of system parameters and yield much better control performance compared with the conventionel PID controller.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.11-18
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• 1995

This paper proposes a nonlinear adaptive equalizer which is based on fuzzy rules and fuzzy inference of several affine mapping for the received channel data. The proposed nolonlinear adaptive equalizers with the significantly lower computational complexity. Also it can be applied to the on-line adaptation environments owing to its fast convergence characteristics and the lower computational load. When using the decision feedback vectors, this equaalizer can be easily realized in the form of the DFE structure with out the requirement for the perfect channel knowledge as in the case of the fuzzy adaptive filter.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.452-455
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• 2003

Based on a field-oriented model of induction motor, adaptive backstepping approach using neural network(RBFN) is proposed for the control of induction motor in this paper. In order to achieve the speed regulation with the consideration of avoiding singularity and improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. rotor resistance uncertainty is compensated by adaptive backstepping and mechanical lumped uncertainty such as load torque disturbance, inertia moment, friction by RBFN. Simulation is provided to verify the effectiveness of the proposed approach.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.57-64
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• 2015

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2294-2314
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• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.