• 대한기계학회논문집A
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• 제37권7호
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• pp.921-927
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• 2013

본 연구는 운용 파라미터의 불확실성을 고려한 착륙장치 완충성능 해석 기법을 제시한다. 실제 운용 환경에서 완충성능에 영향을 미치는 많은 파라미터는 어느 정도의 불확실성을 가지게 되는데, 완충장치 가스 압력과 오일 체적, 타이어 압력, 외부 온도 등을 예로 들 수 있다. 본 연구에서는 Convex Modeling 과 Interval Analysis 기법을 적용하여, 이러한 불확실성이 착륙 시의 지면 반력에 미치는 효과를 해석하였다. 불확실한 파라미터를 고려할 경우, 완충효율 및 구조 건전성에 중요한 영향을 주는 Peak load 가 Deterministic analysis 의 결과보다 크게 증가하였다. 안전성과 신뢰성의 확보를 위해서는 이러한 불확실성을 반영하는 것이 필요하며, 제시한 방법은 이를 효율적으로 처리할 수 있음을 보여준다.

• 한국구조물진단유지관리공학회 논문집
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• 제11권4호
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• pp.138-146
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• 2007

연약지반에 시공된 지반구조물의 거동은 구조물 하부의 연약지반이나 인접 지반의 특성에 크게 영향을 받는다. 지반 구조물 설계시 사용되는 지반 특성치는 결정론적인 모델이나 확률론적인 모델을 이용하여 나타낼 수 있다. 일반적인 지반구조물 설계시 사용되는 결정론적인 모델은 설계 파라메타로 단 하나의 대표값을 사용하는 반면, 확률론적인 모델은 이산통계나 확률 밀도함수를 이용한다. 연약지반의 압밀도나 침하량등의 산정 결과는 간극비, 압밀계수, 연직 및 수평방향 투수계수등의 불확실성 요소에 영향을 받는다. 본 연구에서는 연직 드레인을 이용한 개량지반에 대한 침하해석시 사용되는 불확실성 인자들에 대한 영향을 분석하여 보았다. 또한, 변동계수가 침하량에 미치는 영향을 분석하였으며 이들 파라메타들이 압밀도나 침하계산시 미치는 영향에 대한 민감도 분석을 실시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.159-162
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• 1997

This paper describes robust stabilization of nonlinear single-input uncertain systems without matching conditions. We consider nonlinear systems with a vector of unknown constant parameters perturbed about a known value. The approach utilizes the generalized controller canonical form to lump the unmatched uncertainties recursively into the matched ones. This can be achieved via nonlinear coordinate transformations which depend not only on the states of the nonlinear system but also on the control input. Then the dynamic robust control law is derived and the stability result is also presented.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.436-439
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• 2000

To interact with surroundings robots and other automated machines employ sensors. However, the sensors measurements are uncertain in some amount and this may limit the potential applications of machines. Varying surrounding conditions and noises of sensor signals are major sources of the uncertainty. In this paper the uncertainty is tried to be reduced by using redundant sensor systems, where the redundancy is accomplished by varying the parameters of logically defined sensors. The target value redundantly measured is estimated adaptively. An experiment was done for measuring the stereoscopic 3D position measurement in an inconsistent light condition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.57-61
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• 1989

An adaptive control scheme is presented for uncertain systems whose uncertaintiy bounds are expressed as a linear combination of unknown functions of special form. Both the states and the parameter estimate errors of the closed-loop system are proven to be bounded. The regulation errors can be made sufficiently small by adjusting the design parameters. An application of the proposed method to the position control of a simple pendulum is given.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.39.2-39
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• 2001

This paper describes the design of an output-feedback controller based on an adaptive fuzzy observer for uncertain single-input single-output nonlinear dynamical systems. Especially, we have focused on the realization of minimal dynamic order of the adaptive fuzzy observer. For the purpose, we propose a new method in which no strictly positive real(SPR) condition is needed and combine dynamic rule activation scheme with on-line estimation of fuzzy parameters. By using proposed scheme, we can reduce computation time, storage space, and dynamic order of the adaptive fuzzy observer ...

• 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.5-9
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• 1995

In many cases of robust stability problems, the characteristic polynomial has real coefficients which or nonlinear functions of uncertain parameters. For this set of polynomials, a new stability easily checking algorithm for reducing the conservatism of the stability bound are given. It is the new stability theorem to determine the stability region just in parameter space. Illustrative example show that the presented method has larger stability bound in uncertain parameter space than others.

• 전자공학회논문지S
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• 제34S권6호
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• pp.1-10
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• 1997

This paper presents an adaptive optimal learning controller for uncertian robot systems which makes use fo simple DNN(dynamic neural network) units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a lyapunov function, it is shown that all that error signals in the system are bounded and the robot trajectory converges to the desired one globally exponentially. The effectiveness of the proposed controller is hsown by applying the controller to a 2-DOF robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.87.2-87
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is an uncertain time-variant system. Control synthesis based on a reduced design model is a combined ...

• 제어로봇시스템학회논문지
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• 제3권6호
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• pp.555-562
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• 1997

With only the classical PID controller applied to control of a DC motor, good (target) performance characteristic of the controller can be obtained if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are known exactly. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee good performance, which is assumed with precisely known system parameters and operating conditions. In view of this and the robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one world wide asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing its superiority to the conventional fixed PID controller.