• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.279-282
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• 1995

In this paper, a moving horizon control algorithm, which can be applied for a wide class of nonlinear systems with control and state constraints, is considered. In a neighborhood of the origin, a linear feedback controller is applied. Outside this neighborhood, a moving horizon control law is applied. The time taken to solve an optimal control problem is considered in the algorithm so that the proposed control law can be applied as an on-line controller.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.83-88
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• 2001

The robust linear H(sub)$\infty$ FIR filter, which guarantees a prescribed H(sub)$\infty$ performance, is designed for continuous time-varying systems with unknown cone-bounded nonlinearity. The infinite horizon filtering for time-varying systems is systems is investigated in therms of two Riccati equations by the finite moving horizon.

• Transactions on Control, Automation and Systems Engineering
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• 제2권4호
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• pp.255-261
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• 2000

This paper presents the results of the robust H(sub)$\infty$ FIR filtering for a class of nonlinear continuous time-varying systems subject to real norm-bounded parameter uncertainty and know Lipschitz nonlinearity under sampled measurements. We address the problem of designing filters, using sampled measurements, which guarantee a prescribed H(sub)$\infty$ performance in continuous time-varying context, irrespective of the parameter uncertainty and unknown initial states. The infinite horizon causal H(sub)$\infty$FIR filter are investigated using the finite moving horizon in terms of two Riccati equations with finite discrete jumps.

• 제어로봇시스템학회논문지
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• 제20권3호
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• pp.277-288
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• 2014

This article reviews various state estimation methods for nonlinear systems, particularly with a perspective of a process control engineer. Nonlinear state estimation methods can be classified into the following two categories: stochastic approaches and deterministic approaches. The current review compares the Bayesian approach, which is mainly a stochastic approach, and the MHE (Moving Horizon Estimation) approach, which is mainly a deterministic approach. Though both methods are reviewed, emphasis is given to the latter as it is particularly well-suited to highly nonlinear systems with slow sampling rates, which are common in chemical process applications. Recent developments in underlying theories and supporting numerical algorithms for MHE are reviewed. Thanks to these developments, applications to large-scale and complex chemical processes are beginning to show up but they are still limited at this point owing to the high numerical complexity of the method.

• Industrial Engineering and Management Systems
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• 제8권3호
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• pp.181-193
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• 2009

This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.

• 한국시뮬레이션학회논문지
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• 제17권2호
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• pp.91-103
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• 2008

자동화 제조 시스템에 시뮬레이션 기법을 사용하기 인해서는 자재취급 시스템의 성과를 동적으로 측정해야만 한다. 다중 에이전트 기술은 제조시스템을 지능화 시키고, 분산처리 된 시뮬레이터 개발에 적합하다. 다중 에이전트 시스템은 하나의 조정 에이전트와 다수의 응용 에이전트들로 구성된다. AGVS 시뮬레이터에 있어 이슈는 운반차량의 대수 결정, 양단방향 흐름에서의 이동경로 결정등과 같은 set-up문제와 운영문제로 구분 지을 수 있다. 본 논문에서는 다중 에이전트 기술을 사용하고, 실시간 교착상태 해법 알고리즘이 포함된 시뮬레이터를 소개한다. 시뮬레이터는 잘 알려진 프로이드(Floyd) 알고리즘을 사용하여 AGVS의 최단 이동경로를 구성된다. 움직이고 있는 차량 에이전트는 조정 에이전트에 의해 실시간 제어로 작동되고, AGV는 경로확인 알고리즘을 사용하여 충돌과 교착상태를 피한다. AGV의 위치는 수평시간 계획법에 근거하여 동적으로 재계산된다. 충돌해소 알고리즘은 어떠한 배치 형태에서라도, 그리고 큰 규모의 문제에서도 AGVS의 운영 중의 주행문제 해결을 보장한다. 시뮬레이터는 AGV들의 AGVS 칸트차트를 통하여 작동정보를 받고, 표현한다. 제안된 알고리즘의 성과와 다중 에이전트 기술을 사용하여 개발한 시뮬레이터는 실험을 통하여 검증된다.