• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.206-209
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• 1988

For feedback control of a linear dynamic system the optimum linear slace regulator (OLSR) can be implemented only if all state are available for feedback. This work demonstrates that when only the output state is available for feedback, a nonlinear controllers can be improved performance over that obtained by a proportional controller. This paper found the optimal control law by well-known dynamic programming and principles of optimality. Thus, performance of both proportional and nonlinear controllers is compared with performance of optimum linear state regulator.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.50-56
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• 2000

This paper presents the design of an optimal state feedback controller for container cranes under some design specifications. To do this, the nonlinear equation of a container crane system is linearized and then augmented to eliminate the steady-state error, and some constraints are derived from the design specifications. Designing the controller involves a constrained optimization problem which classical gradient-based methods have difficulties in handling. Therefore, a real-coding genetic algorithm incorporating the penalty strategy is used. The responses of the proposed control system are compared with those of the unconstrained optimal control system to illustrate the efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.438-443
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• 2003

In this paper, we propose a control algorithm for two-wheel mobile robot that can move the rider to his or her command and autonomously keep its balance. The control algorithm is based on a mixed $H_2/H_{\infty}$ control scheme. In this control problem the main issue is to move the rider while keeping its balance in the presence of disturbances and parameter uncertainties. The disturbance force caused by uneven road surfaces and the uncertainty due to different rider's heights are considered. To this end we first consider a state feedback controller as a basic framework. Secondly, we obtain the state feedback gain $K_2$ minimizing the $H_2$ norm and the state feedback gain $K_{\infty}$ minimizing the $H_{\infty}$ norm over the whole range of parameter uncertainty. Finally, we select mixed $H_2$/$H_{\infty}$ state feedback controller K as the geometric mean of $K_2$ and $K_{\infty}$. Simulation results show that the mixed $H_2/H_{\infty}$ state feedback controller combines the effects of the optimal $H_2$ state feedback controller and robust $H_{\infty}$ controller state feedback controller efficiently in the presence of disturbance and parameter uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.172.2-172
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• 2001

We introduce a new stable feedback controller eventually converges to a conventional SDRE(State Dependent Riccati Equation) based optimal (suboptimal) controller. On conventional SDRE, the optimal control input should be obtained by backward integration of the SDRE at each control point. The proposed controller is given by direct forward integration of a proposed SDRE. This fact enables fast computation and easy implementation. On concerning a state dependent system, the proposed controller may be a candidate to the conventional SDRE based optimal controller if the system is slow varying with states. Though the controller is fast and easy to implement it is not able to cope with a fast varying system. We introduce an optimality index, which indicates how far the proposed controller is deviated from the solution of the convectional SDRE. If the index escapes a ...

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.510-512
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• 1999

In this paper, we present a method for the analysis and design of fuzzy controller for nonlinear systems. In the design procedure, we represent the dynamics of nonlinear systems using a Takagi-Sugeno fuzzy model and formulate the controller rules, which shares the same fuzzy sets with the fuzzy system, using parallel distributed compensation method. Then, after the feedback gain of each local state feedback controller is obtained using the existing optimal pole-placement scheme, we construct an overall fuzzy logic controller by blending all local state feedback controller. Finally, the effectiveness and feasibility of the proposed fuzzy-model-based controller design method has been evaluated through an inverted pendulum system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.51-53
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• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• 한국지능시스템학회논문지
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• 제20권6호
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• pp.768-773
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• 2010

본 논문에서는 도립진자 시스템을 이용한 최적 상태 되먹임 제어기 설계 및 성능 검증을 소개한다. 제안하고 있는 최적 상태 되먹임 제어기는 최적제어 이론을 기반으로 입력크기와 응답성능의 최적값을 고려하였고 뿐만 아니라, 적분제어기법을 도입함으로써 정상상태오차를 줄이도록 하였다. 실험에 사용된 도립진자 시스템은 (주)셈웨어의 PC기반 CEM-IP-01으로써 제안된 제어기를 적용한다. 마지막으로 제안된 제어기와 일반 상태 되먹임 제어기를 모의실험과 실험을 통하여 비교함으로써 제안된 제어기의 성능을 검증한다.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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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.

• 한국정밀공학회지
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• 제6권1호
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• pp.85-92
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• 1989

For feedback control of a linear dynamic system the optimum linear state regulator (OLSR) can be implemented only if all states are available for feedback. This work demonstrates that when only the output state is available for feedback, a nonlinear controllers can give improved performance over that obtained by a proportional controller. This paper found the optimal control law by dynamic programming and principles of optimalityl. This, performances of both proportional and nonlinear controllers are compared with performance of optimum linear state regulator.

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.34-41
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• 1999

This paper presents a HGA-based(hybrid genetic algorithm) optimal control strategy to control of the swing motion and the transfer of the overhead crane. The objective is to achieve the regulation of the fast swing motion or fast position control. The controller is based on the state feedback. The HGA-based optimal algorithm is applied to find optimal gains of the controller. Computer simulation and experiments were performed to demonstrate the effectiveness of the proposed control scheme.