• Journal of Information Processing Systems
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• 제13권2호
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• pp.417-427
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• 2017

Power allocation is an important factor for cognitive radio networks to achieve higher communication capacity and faster equilibrium. This paper considers power allocation problem to each cognitive user to maximize capacity of the cognitive systems subject to the constraints on the total power of each cognitive user and the interference levels of the primary user. Since this power control problem can be formulated as a mixed-integer nonlinear programming (NP) equivalent to variational inequality (VI) problem in convex polyhedron which can be transformed into complementary problem (CP), we utilize modified projection method to solve this CP problem instead of finding NP solution and give a power control allocation algorithm with a subcarrier allocation scheme. Simulation results show that the proposed algorithm performs well and effectively reduces the system power consumption with almost maximum capacity while achieve Nash equilibrium.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.659-664
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• 2000

Fuel-consumption and catalyst-out emissions of a parallel hybrid electric vehicle are affected by operating region of an engine. In many researches, It is generally known that it is profitable in fuel- consumption to operate engine in OOL(Optimal Operating Line). We established the mathematical model of a parallel hybrid electric vehicle, which is linear time-invariant. To operate an engine in OOL, we applied RHC(Receding Horizon Control) to the driving control of a parallel hybrid electric vehicle. And it is known that the RHC has advantages such as good tracking performance under state and control constraints. This RHC is obtained by using linear matrix inequality (LMI) optimization. In this paper, there are three main topics. First, without state and control constraints, the optimal tracking of OOL was simulated. Second, with state and control constraints by engine and motor performances, the optimal tracking of OOL was simulated. In the last, we studied on the optimal gear ratio. That is to say, we combined the RHC and the iterative simulation to extract the optimal gear ratio. In this simulation, the vehicle is commanded to track the reference vehicle trajectory and the engine is operated in the optimal operating region which is made by the state constraints.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.34.1-34
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• 2001

We deal with robust control problem for singularly perturbed linear systems with norm-bounded structured uncertainty under state constraints. We assume that the norm-bounded uncertainty is composed of repeated scalar-block and full-block forms. In the structured uncertainty, repeated scalar block forms account for uncertain physical parameter value and full-block forms may be some unknown nonlinear dynamics. In order deal with uncertainty and state constraints, we use LMI(Linear Matrix Inequality). The original problem is decomposed into two well behaved reduced order problems. Shinc two LMI problems are completely independent, each solution can be computed simultaneously and work in parallel.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.60-63
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• 1996

This study deals with the nonlinear H$_{\infty}$ control problem of linear system using nonlinear weight. Generally the solvable condition of nonlinear H$_{\infty}$ control problem is given by the Hamilton Jacobi equality or inequality, but it is very difficult to solve. In this study, some constraints of nonlinear weight reduce the solvable condition to linear Riccati equation. Some examples of the control system design using nonlinear weight are shown.n.

• 한국항공우주학회지
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• 제30권8호
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• pp.71-77
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• 2002

본 논문에서는 강성 중앙동체에 끝단 질량을 갖는 두 개의 유연구조물이 부착되어 있는 구조 모델의 선회기동과 진동억제를 동시에 제어하는 문제를 고려하였다. 구조모델의 선형 운동방정식을 구하기 위해 유한요소법을 사용하였다. 우주구조물 모델의 LQR문제에 있어서 물리적 의미를 갖는 성능지수를 제공하는 간단한 방법을 제안하였다. 제안된 성능지수는 일반적으로 사용하는 에너지 형식의 성능지수와 비교할 때 수학적으로는 큰 차이가 없으나 물리적으로는 의미있는 차이를 갖는다. 특수해 방법을 사용하여 부등호 제어 제약조건이 있는 시변 LQR문제를 해결하는 수치적 절차를 소개하였다.

• 대한전기학회논문지
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• 제32권4호
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• pp.121-127
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• 1983

In this paper an efficient algorithm for Pontriagin's maximum principle is developed. Fletcher-Powell method is adopted as optimization technique which shows fast and stable convergence characteristics. Terminal constraints are alse considered by using Hestens' algorithm and penalty function method together. Control variable inequality constraints are also considered by using Gradient Projection technique combined with Flectcher-Powell method. Test experiment shows good and reliable results.

• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1212-1217
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• 2008

This paper studies the feasibility of using the nonlinear model predictive control as a formation flight control algorithm for unmanned aerial vehicles. The optimal control inputs for formation flight are calculated through the cost function which incorporates the relative positions of the individual vehicles to maintain a desired formation and also the inequality constraints on inputs and states using the Karush-Kuhn-Tucker conditions. In the nonlinear model predictive control setting, the optimal control inputs are implemented in a receding horizon manner, which is suitable for dealing with dynamic constraints. Numerical simulations are executed for the validation of the proposed scheme.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.143-151
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• 2005

This paper considers a robust decentralized $H_\infty$ control problem for uncertain large-scale interconnected systems. The uncertainties are assumed to be time-invariant, norm-bounded, and exist in subsystems. A design method based on the bounded real lemma is developed for a dynamic output feedback controller, which is reduced to a feasibility problem for a nonlinear matrix inequality (NMI). It is proposed to solve the NMI iteratively by the idea of homotopy, where some of the variables are fixed alternately on each iteration to reduce the NMI to a linear matrix inequality (LMI). A decentralized controller for the nominal system is computed first by imposing structural constraints on the coefficient matrices gradually. Then, the decentralized controller is modified again gradually to cope with the uncertainties. A given example shows the efficiency of this method.

• 전기학회논문지
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• 제61권7호
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• pp.1019-1023
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• 2012

This paper considers a model predictive control problem of discrete-time constrained systems with time-varying delay. For this problem, a delay dependent state feedback control approach is used to achieve asymptotic stabilization of systems with input constraints. Based on Lyapunov stability theory, a new stability condition is obtained via linear matrix inequality formulation to find cost monotonicity condition of the model predictive control algorithm which guarantee the closed loop stability. Finally, the proposed method is applied to a numerical example in order to show the effectiveness of our results.

• 대한수학회지
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• 제41권5호
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• pp.821-864
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• 2004

Both parametric and parameter-free necessary and sufficient optimality conditions are established for a class of nondiffer-entiable nonconvex optimal control problems with generalized fractional objective functions, linear dynamics, and nonlinear inequality constraints on both the state and control variables. Based on these optimality results, ten Wolfe-type parametric and parameter-free duality models are formulated and weak, strong, and strict converse duality theorems are proved. These duality results contain, as special cases, similar results for minmax fractional optimal control problems involving square roots of positive semi definite quadratic forms, and for optimal control problems with fractional, discrete max, and conventional objective functions, which are particular cases of the main problem considered in this paper. The duality models presented here contain various extensions of a number of existing duality formulations for convex control problems, and subsume continuous-time generalizations of a great variety of similar dual problems investigated previously in the area of finite-dimensional nonlinear programming.