• 제어로봇시스템학회논문지
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• 제11권1호
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• pp.77-83
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• 2005

This paper deals with determination of motions of a humanoid robot using genetic algorithm. A humanoid robot has some problems of the structural instability basically. So, we have to consider the stable walking gait in gait planning. Besides, it is important to make the smoothly optimal gait for saving the electric power. A mobile robot has a battery to move autonomously. But a humanoid robot needs more electric power in order to drive many joints. So, if movements of walking joints don't maintain optimally, it is difficult for a robot to have working time for a long time. Also, if a gait trajectory doesn't have optimal state, the expected life span of joints tends to be decreased. To solve these problems, the genetic algorithm is employed to guarantee the optimal gait trajectory. The fitness functions in a genetic algorithm are introduced to find out optimal trajectory, which enables the robot to have the less reduced jerk of joints and get smooth movement. With these all process accomplished by a PC-based program, the optimal solution could be obtained from the simulation. In addition, we discuss the design consideration for the joint motion and distributed computation of the humanoid, ISHURO, and suggest its result such as the structure of the network and a disturbance observer.

• 대한전자공학회논문지TE
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• 제42권3호
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• pp.25-30
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• 2005

대부분의 경우, 비선형 최적 제어 문제는 헤밀톤-야코비 방정식(Hamilton-Jacobi equations)을 풀어야하는데, HJEs는 해석적으로 답을 구하기가 매우 어렵다. 그래서 이러한 어려움은 비선형 시스템에 피드백 선형화를 적용하여, 선형화된 시스템을 얻고, 선형화된 선형 시스템에 대한 최적 제어 문제를 고려하게 되었다. 본 논문에서는 간단한 비선형 시스템의 예에 최적 제어 설계 기법과 피드백 선형화 제어기, 선형 제어기를 적용하여, 최적 성능을 평가함으로서, 피드백 선형화 최적 제어가 적용되는 비선형 시스템의 조건을 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.729-733
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• 1988

In this paper, a new procedure for selecting weighting matrices in linear discrete time quadratic optimal control problems (LQ-problem) is proposed. In LQ problems, the quadratic weighting matrices are usually decided on trial and error in order to get a good response. But using the proposed method, the quadratic weights are decided in such a way that all poles of the closed loop system are located in a desired area for good responses as well as for stability and values of the quadratic cost functional are kept less then a specified value. The closed loop systems constructed by this method have merits of LQ problems as well as those of pole assignment problems. Taking into consideration that little is known about the relationship among the quadratic weights, the poles and the values of cost functional, this procedure is also interesting from the theoretical point of view.

• Journal of Power Electronics
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• 제19권1호
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• pp.119-133
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• 2019

Although the dual modulation wave method can solve the low-frequency neutral point potential (NPP) fluctuation problem for three-level neutral point clamped converters, it also increases the switching frequency and limits the zero-sequence voltage. That makes it harmful when dealing with the NPP drift problem if the converter suffers from a long dead time or asymmetric loads. By introducing two degrees of freedom (2-DOF), an NPP control based on a search optimization method can demonstrate its ability to cope with the above mentioned two types of NPP problems. However, the amount of calculations for obtaining an optimal 2-DOF is so large that the method cannot be applied to certain industrial applications with an inexpensive digital signal processor. In this paper, a novel optimal 2-DOF-based NPP control is proposed. The relationships between the NPP and the 2-DOF are analyzed and a method for directly determining the optimal 2-DOF is also discussed. Using a direct calculation method, the amount of calculations is significantly reduced. In addition, the proposed method is able to maintain the strongest control ability for the two types of NPP problems. Finally, some experimental results are given to confirm the validity and feasibility of the proposed method.

• 대한전기학회논문지
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• 제39권10호
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• pp.1075-1085
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• 1990

This paper presents a new method for the optimal control of the distributed parameter systems by a decentralized computational procedure. Approximate lumped parameter models are derived by using the Galerkin method employing the Legendre polynomials as the basis functions. The distributed parameter systems, however, are transformed into the large scale lumped parameter models. And thus, the decentralized control scheme is introduced to determine the optimal control inputs for the obtained lumped parameter models. In addition, an approach to block pulse functions is applied to solve the optimal control problems of the obtained lumped parameter models. The proposed method is simple and efficient in computation for the optimal control of distributed paramter systems. Illustrative examples given to demonstrate the validity of the presently proposed method.

• 대한수학회지
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• 제45권3호
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• pp.757-769
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• 2008

This paper deals with the existence of optimal controls and maximal principles for semilinear evolution equations with the nonlinear term satisfying Lipschitz continuity. We also present the necessary conditions of optimality which are described by the adjoint state corresponding to the linear equations without a condition of differentiability for nonlinear term.

• Journal of applied mathematics & informatics
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• 제19권1_2호
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• pp.215-229
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• 2005

In this paper, constraint aggregation is combined with the adjoint and multiple shooting strategies for optimal control of differential algebraic equations (DAE) systems. The approach retains the inherent parallelism of the conventional multiple shooting method, while also being much more efficient for large scale problems. Constraint aggregation is employed to reduce the number of nonlinear continuity constraints in each multiple shooting interval, and its derivatives are computed by the adjoint DAE solver DASPKADJOINT together with ADIFOR and TAMC, the automatic differentiation software for forward and reverse mode, respectively. Numerical experiments demonstrate the effectiveness of the approach.

• 대한전기학회논문지
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• 제36권4호
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• pp.301-304
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• 1987

The Optimal Control Problems for the singular system with the Generalized state space model are considered. It is shown that when the system is singular, the dimension can be reduced by coordinate transformation and the equivalent nonsingular system is got. After we have nonsingular system, the solution for the optimal control problem can be got by Riccati equation.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.525-533
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• 1998

A conventional contact type brake system which uses a hydraulic system has mny Problems such as time delay response due to pressure build-up, brake pad wear due to contact movement, bulky size, and low braking performance in high speed region. As vehicle speed increases, a more powerful brake system is required to ensure vehicle safety and reliability. In this work, a contactless brake system of an eddy current type is proposed to overcome problems. Optimal torque control which minimizes a braking distance is investigated with a scaled-down model of an eddy current type brake. It is possible to realize optimal torque control when a maximum friction coefficient (or desired slip ratio) corresponding to road condition is maintained. Braking force analysis for a scaled-down model is done theoretically and experimentally compensated. To accomplish optimal torque control of an eddy current type brake system, a sliding mode control technique which is, one of the robust nonlinear control technique is developed. Robustness of the sliding mode controller is verified by investigating the braking performance when friction coefficient is varied. Simulation and experimental results will be presented to show that it has superior performance compared to the conventional method.

• 한국시뮬레이션학회논문지
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• 제5권1호
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.