• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1740-1751
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• 2018

This paper proposes a novel fault tolerant tracking control (FTTC) scheme for a class of nonlinear systems with actuator failures based on the policy iteration (PI) algorithm and the adaptive fault observer. The estimated actuator failure from an adaptive fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. With the help of the proper performance index function, the FTTC problem can be transformed into an optimal control problem. The fault tolerant tracking controller is composed of the desired controller and the approximated optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, and the approximated optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. By establishing a critic neural network, the PI algorithm is utilized to solve the Hamilton-Jacobi-Bellman equation, and then the approximated optimal feedback controller can be derived. Based on Lyapunov technique, the uniform ultimate boundedness of the closed-loop system is proven. The proposed FTTC scheme is applied to reconfigurable manipulators with two degree of freedoms in order to test the effectiveness via numerical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.584-588
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• 1991

This paper presents a fuzzy adaptive controller which can improve the control policy automatically. Adaptation is achieved by the addition of on-line identification of the fuzzy inverse model using input-output data pairs of the process. Starting with an initial crude control rule, the adaptive controller matches the model to the process to self-tune the controller. The control algorithm needs much less memory of computer than other SOC algorithms.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.124-127
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• 1997

A key element in the control of Automated Guided Vehicle Systems (AGVS) is dispatching policy. This paper proposes a new dispatching algorithm for an efficient operation of AGVS. Based on an evolutionary operation, it has an adaptive control capability responding to changes of the system environment. The performance of the algorithm is compared with some well-known dispatching rules in terms of the system throughput through simulation. Sensitivity analysis is carried out varying the buffer capacity and the number of AGVS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.73-79
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• 1994

This paper propose Adaptive Maintenance as a new type of maintenance for machine failures which are unpredictable. A purpose of adpative maintenance is to decrease inconsistency. In order to pick up some of problems the traditional maintenance policy, We discussed Time Based Maintenance(TBM) and Condition Based Maintenance(CBM) with Bath-Tub Curve. By using Machine Condition Diagnosis Technique (CDT), Monitored condition maintenance deals with the dynamic decision making for diagnosis procedures at maintenance and caution level. Adaptive Maintenance is a powerful tool for Total Production Maintenance(TPM).

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.117-119
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• 2000

UMTS(Universal Mobile Telecommunication System)의 Core Network에서는 SGSN(Serving GPRS Support Node)과 GGSN(Gateway GPRS Support Node)사이의 전송계층을 IP-Over-ATM network을 기반으로 한다. 이는 비연결형(connectionless) IP 트래픽들을 연결지향형(connectionoriented) ATM 전송계층을 통해 전송함으로 이때 효율적인 자원관리를 위해 적절한 VC(Virtual Circuit)의 접속과 해제를 수행해야 한다. 본 논문에서는 [1]에서 제안되는 GPRS(General Packer Radio Service)의 Web 트래픽 모델에 대하여 기존의 VC의 Holding-time을 결정하는 기법인 Holding Cost Pricing Model(LRU, Mean-Variance, Adaptive policy) [2]을 GGSN과 SGSN사이의 ATM 전송계층에 적용하였다. 각각의 기법들에서 VC의 이용률(utilization)과 설정율(setup rate)을 비교함으로써 Adaptive policy 기법의 성능이 다른 기법에 비해 효율적임을 알 수 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1118-1124
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• 2014

This paper proposes a novel controller design scheme for multimachine power systems based on the explorized policy iteration. Power systems have several uncertainties on system dynamics due to the various effects of interconnections between generators. To solve this problem, the proposed method solves the LQR (Linear Quadratic Regulation) problem of isolated subsystems without the knowledge of a system matrix and the interconnection parameters of multimachine power systems. By selecting the proper performance indices, it guarantees the stability and convergence of the LQ optimal control. To implement the proposed scheme, the least squares based online method is also investigated in terms of PE (Persistency of Excitation), interconnection parameters and exploration signals. Finally, the performance and effectiveness of the proposed algorithm are demonstrated by numerical simulations of three-machine power systems with governor controllers.

• ETRI Journal
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• v.28 no.6
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• pp.815-818
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• 2006

In the global Internet, a constraint-based routing algorithm performs the function of selecting a routing path while satisfying some given constraints rather than selecting the shortest path based on physical topology. It is necessary for constraint-based routing to disseminate and update link state information. The triggering policy of link state updates significantly affects the volume of update traffic and the quality of services (QoS). In this letter, we propose an adaptive triggering policy based on link-usage statistics in order to reduce the volume of link state update traffic without deterioration of QoS. Also, we evaluate the performance of the proposed policy via simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.451-458
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• 2012

This paper addresses the problem that policy iteration (PI) for continuous-time (CT) systems requires explorations of the state space which is known as persistency of excitation in adaptive control community, and as a result, proposes a PI scheme explorized by an additional probing signal to solve the addressed problem. The proposed PI method efficiently finds in online fashion the related CT linear quadratic (LQ) optimal control without knowing the system matrix A, and guarantees the stability and convergence to the LQ optimal control, which is proven in this paper in the presence of the probing signal. A design method for the probing signal is also presented to balance the exploration of the state space and the control performance. Finally, several simulation results are provided to verify the effectiveness of the proposed explorized PI method.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.33-49
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• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.408-418
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• 2007

In this paper, we address the question of dynamic network provisioning for time-varying traffic rates, with the objective of maximizing the system throughput. We assume that the network is capable of providing bandwidth guaranteed traffic tunnels for an ingress-egress pair and present an approach that (1) updates the tunnel routes and (2) adjusts the tunnel bandwidths, in an incremental, adaptive manner, based on the variations in the incoming traffic. First, we consider a simpler scenario where tunnel routes are fixed, and present an approach for adjusting the tunnel bandwidths dynamically. We show, through simulations, that our dynamic bandwidth assignment algorithm significantly outperforms the optimal static bandwidth provisioning policy, and yields a performance close to that of the optimal dynamic bandwidth provisioning policy. We also propose an adaptive route update algorithm, which can be used in conjunction with our dynamic bandwidth assignment policy, and leads to further improvement in the overall system performance.