• 제어로봇시스템학회논문지
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• 제20권2호
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• pp.170-174
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• 2014

In this paper, an unknown input estimation method via the optimal FIR smoother is proposed for linear discrete-time systems. The unknown inputs are represented by random walk processes and treated as auxiliary states in augmented state space models. In order to estimate augmented states which include unknown inputs, the optimal FIR smoother is applied to the augmented state space model. Since the optimal FIR smoother is unbiased and independent of any a priori information of the augmented state, the estimates of each unknown input are independent of the initial state and of other unknown inputs. Moreover, the proposed method can be applied to stochastic singular systems, since the optimal FIR smoother is derived without the assumption that the system matrix is nonsingular. A numerical example is given to show the performance of the proposed estimation method.

• 한국경영과학회지
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• 제29권4호
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• pp.141-157
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• 2004

In this paper, we consider broadband satellite systems using MF-TDMA(Multi-Frequency Time Division Multiple Access) scheme. First, we analyze return link, superframe structure, and QoS( Quality of Service) parameters in broadband satellite systems, and mathematically formulate the QoS-based optimal timeslot allocation problem as a nonlinear integer programming problem for broadband satellite systems with clear-sky and rain-fade satellite terminals, and multiple data classes. Next, we modify the proposed problem to solve it within in a fast time, and suggest the QoS-based optimal timeslot allocation scheme. Extensive simulation results show that the proposed scheme finds an optimal solution or a near optimal solution within 5ms at Pentium IV PC.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.354-362
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• 2000

This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposed method is easily applicable to the analysis and optimal control of nonlinear systems.

• International Journal of Control, Automation, and Systems
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• 제5권5호
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• pp.515-525
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• 2007

In this paper, we develop a co-design methodology of stochastic optimal controllers and network parameters that optimizes the overall quality of control (QoC) in networked control systems (NCSs). A new dynamic model for NCSs is provided. The relationship between the system stability and performance and the sampling frequency is investigated, and the analysis of co-design of control and network parameters is presented to determine the working range of the sampling frequency in an NCS. This optimal sampling frequency range is derived based on the system dynamics and the network characteristics such as data rate, time-delay upper bound, data-packet size, and device processing time. With the optimal sampling frequency, stochastic optimal controllers are designed to improve the overall QoC in an NCS. This co-design methodology is a useful rule of thumb to choose the network and control parameters for NCS implementation. The feasibility and effectiveness of this co-design methodology is verified experimentally by our NCS test bed, a ball magnetic-levitation (maglev) system.

• 전기학회논문지
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• 제59권5호
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• pp.882-889
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• 2010

This paper presents a method of finding the optimal operating point minimizing a given objective function with 3 phase power flow equations and operational constraints, called 3 phase optimal power flow (3POPF). 3 phase optimal power flow can provide operation and control strategies for the distribution systems with distributed generation assets, which might be frequently in unbalanced conditions assuming that high penetration rate of renewable energy sources in the systems. As the solution technique for 3POPF, this paper adopts a simulation-based method of particle swarm optimization (PSO). In the PSO based 3POPF, a utility function needs to be defined for evaluation of the degree in operational improvement of each particle's current position. To evaluate the utility function, in this paper, NR-based 3 phase power flow algorithm was developed which can deal with looped distributed generation systems. In this paper, illustrative examples with a 5-bus and a modified IEEE 37-bus test systems are given.

• Journal of Biosystems Engineering
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• 제11권2호
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• pp.77-87
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• 1986

This study was carried out to design the optimal systems of diversified farming by the mathematical model developed. In order to achieve this goal, a computer program named DFSDINGP was developed by the nonlinear goal programming(NGP), and for testing its effectiveness, the optimal systems of diversified farming were designed for three regions surveyed and compared them with those of the conventional. DFSDINGP was programmed with FORTRAN 77 and it could handle the NGP problem having 25 independent variables and 75 constraint functions. The study results showed that the developed models and DFSDINGP could design the optimal systems of diversified farming satisfying two goals which are maximum agricultural income and maximum power inputs of agricultural machinery. The agricultural incomes and power inputs of farm machinery of the optimal systems were more than those of the conventional as much as 29-62% and 9-134%, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.61-61
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• 2000

Since FMS(Flexible Manufacturing System) such as semiconductor production systems have the characteristic that each device has to be commonly used in several stages, it is difficult to find an optimal solution. In this paper, we proposed the new algorithm which can get the optimal ratio of sharing resources. We will implement the proposed algorithm to semiconductor production systems. We introduce the optimal algorithm, which is modeled and analyzed by ExSpect, a petri net based simulation tool. When there exist conflicts of sharing resources, the scheduling method is adopted, which gives a priority to the most preceded process. The suggested algorithm can be used not only in semiconductor production systems but also in various FMS.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권10호
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• pp.464-472
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• 2001

This study uses distributed parameter systems as the spatial discretization technique, modelling in lumped parameter systems, and applies fast WALSH transform and the Picard's iteration method to high order partial differential equations and matrix partial differential equations. This thesis presents a new algorithm which usefully exercises the optimal control in the distributed parameter systems. In exercising optimal control of distributed parameter systems, excellent consequences are found without using the existing decentralized control or hierarchical control method. This study will help apply to linear time-varying systems and non-linear systems. Further research on algorithm will be required to solve the problems of convergence in case of numerous applicable intervals.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2447-2449
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• 2003

In this paper, optimal fuzzy systems using vector quantization and fuzzy logic controllers are designed for vision-based robot control systems. The complexity of the optimal fuzzy system for vision-based control systems is so great that it can not be applied to real vision-based control systems or it can not be useful, because there are so many input-output pairs. Therefore, we generally use the clustering of input-output pairs, in order to reduce the complexity of optimal fuzzy systems. To increase the effectiveness of the clustering, a vector quantization clustering method is proposed. In order to verify the effectiveness of the proposed method experimentally, it is applied to a vision-based arm robot control system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.73-77
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• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.