• 제어로봇시스템학회논문지
• /
• 제20권4호
• /
• pp.395-401
• /
• 2014

Magnetic levitation systems using the attraction force of electromagnets have many constraints according to the variation of air gap and the nonlinearity of electromagnetic force and inductances. As a result of these constraints, the nonlinear control of a magnetic levitation system has been improved by the latest advanced processors and accurate measurement system which can overcome problems such as many constraints and nonlinearity. This paper concentrates on the modeling of a nonlinear magnetic levitation system and an application of an exponential reaching law based sliding mode controller using the exponential reaching law which is one of the most robust controllers against external unexpected disturbances or parameter fluctuations. Controllability of a magnetic levitation system using the sliding mode control algorithm and robustness against parameter fluctuations have been verified through the experimental results.

• 대한수학회지
• /
• 제41권5호
• /
• pp.821-864
• /
• 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.

• Journal of Electrical Engineering and Technology
• /
• 제4권3호
• /
• pp.337-345
• /
• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• 품질경영학회지
• /
• 제9권2호
• /
• pp.31-36
• /
• 1981

점증되고 있는 고신뢰성 제품의 설계에 있어 주어진 선형제약조건 내(內)에서 체계의 신뢰성을 최대화하는 방법을 소개하고 이를 해결하는 비선형계획법을 반복단계로 하여 Computer Programming을 하였다. 단, 본 논문에서 다루는 체계는 병렬중복구조를 갖는 직렬다단계 구조이다. 타당성 검토를 위한 예제를 해결하였으며 Computer Programming은 지면관계로 생략하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
• /
• pp.697-699
• /
• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 A
• /
• pp.127-129
• /
• 2002

This paper presents a fuzzy-GA method to resolve dispersed generator placement for distribution systems. The problem formulation considers an objective to reduce power loss costs of distribution systems and the constraints with the number or size of dispersed generators and the deviation of the bus voltage. The main idea of solving fuzzy nonlinear goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature and solve the problem using the proposed genetic algorithm, without any transformation for this nonlinear problem to a linear model or other methods. The method proposed is applied to the sample systems to demonstrate its effectiveness.

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

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
• /
• pp.23-26
• /
• 2000

Generally, the wheel-driven mobile robot systems, by their structural property, have nonholonomic constraints. These constraints are not integrable and cannot be written as time derivatives of some functions with respect to the generalized coordinates. Hence, nonlinear approaches are required to solve the problems. In this paper, the trajectory controller of wheeled mobile robot systems is suggested to guarantee its convergence to reference trajectory. Design procedure of the suggested trajectory controller is back-stepping scheme which was introduced recently in nonlinear control theory. The performance of the proposed trajectory controller is verified via computer simulation. In the simulation, the trajectory controller is applied to differentially driven robot system and car-like mobile robot system on the assumption that the trajectory planner be given.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 1989년도 추계학술발표회 발표논문초록집; 이화여자대학교, 서울; 23 Sep. 1989
• /
• pp.45-50
• /
• 1989

The above theorm states that much larger classes of differential games have an equilibrium. The most severe assumption is the second one. It requires that state dynamic equations be linear on his own control variables. But, the dynamic programming approach applied in the above is hardly implementable for the purpose of computation. It is very difficult to solve (SP$_{it}$) directly. Notice, however, the problem can be transformed into a Hamiltonian maximization problem which is easy to solve if initial conditions are given. In this way, it is possible to design a solution algorithm to problems with nonlinear constraints. The above two theorems probide a basis for such an algorithm.m.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권11호
• /
• pp.4387-4404
• /
• 2015

The energy-efficient design of sensing-based spectrum sharing of a multi-input and multi-output (MIMO) cognitive radio (CR) system with imperfect multiple antenna spectrum sensing is investigated in this study. Optimal resource allocation strategies, including sensing time and power allocation schemes, are studied to maximize the energy efficiency (EE) of the secondary base station under the transmit power and interference power constraints. EE problem is formulated as a nonlinear stochastic fractional programming of a nonconvex optimal problem. The EE problem is transformed into its equivalent nonlinear parametric programming and solved by one-dimension search algorithm. To reduce searching complexity, the search range was founded by demonstration. Furthermore, simulation results confirms that an optimal sensing time exists to maximize EE, and shows that EE is affected by the spectrum detection factors and corresponding constraints.