• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.110.4-110
• /
• 2002

In practical control area, there are many examples with multiple objectives which may conflict or compete with each other like overhead crane control, automatic train operation, and refuse incinerator plant control, etc. These kinds of control problems are called multiobjective control problems, where it is difficult to provide the desired performance with control strategies based on single-objective optimization. Because the conventional control theories usually treat the control problem as the single objective optimization problem , the methods are not adequate to treat the multiobjective control problems. Particularly, in case of large scale systems or ill-defined systems, the multiple obj..

• Journal of applied mathematics & informatics
• /
• v.5 no.2
• /
• pp.475-488
• /
• 1998

Wolfe and Mond-Weir type duals for multiobjective con-trol problems are formulated. Under pseudo-invexity/quasi-invexity assumptions of the functions involved, weak and strong duality the-orems are proved to relate efficient solutions of the primal and dual problems.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1371-1376
• /
• 2003

In the thermal power plant, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature, the change of the dynamic characteristics in the reheater. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the 2-DOF PID Controller on the DCS for steam temperature control using immune based multiobjective approach. The stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Therefore tuning technique of multiobjective based on immune network algorithms in this paper can be used effectively in tuning 2-DOF PID controllers.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.271-281
• /
• 2003

In this paper, a new nonlinear programming approach is suggested to solve biaffine matrix inequality (BMI) problems in multiobjective and structured controls. It is shown that these BMI problems are reduced to nonlinear minimization problems. An algorithm that is easily implemented with existing convex optimization codes is presented for the nonlinear minimization problem. The efficiency of the proposed algorithm is illustrated by numerical examples.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.525-530
• /
• 2003

This paper focuses on design of nonlinear power plant controller using immune based multiobjective fuzzy approach. The thermal power plant is typically regulated by the fuel flow rate, the spray flow rate, and the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature. the change of the dynamic characteristics in the steam-turbine system. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. These parameters tuned by multiobjective based on immune network algorithms could be used for the tuning of nonlinear power plant.

• Journal of Mechanical Science and Technology
• /
• v.19 no.5
• /
• pp.1138-1147
• /
• 2005

This paper aims at demonstrating the feasibility of active control of beams with a multiobjective state-feedback control technique. The multiobjective state-feedback controller is de­signed on a linear matrix inequality (LMI) approach for the multiobjective synthesis. The design objectives are to achieve a mix of Hoo performance and H2 performance satisfying constraints on the closed-loop pole locations in the face of model uncertainties. The controller is also designed to reject the effects of the noise and external of disturbances. For the theoretical analysis, the governing equation of motion is derived by Hamilton's principle to describe the dynamics of a smart structural system. Numerical examples are presented to demonstrate the effectiveness of the integrated robust controller in damping out the multiple vibration modes of the piezo/beam system.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.677-680
• /
• 2000

The technique of reinforcement learning algorithm is extended to solve the multiobjective control problem for uncertain dynamic systems. A multiobjective adaptive critic structure is proposed in order to realize a max-min method in the reinforcement learning process. Also, the proposed reinforcement learning technique is applied to a multiobjective satisfactory fuzzy logic controller design in which fuzzy logic subcontrollers are assumed to be derived from human experts. Some simulation results are given in order to show effectiveness of the proposed method.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.1
• /
• pp.70-78
• /
• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.11a
• /
• pp.452-458
• /
• 2003

This paper presents a robust vibration control methodology of smart structural systems. The governing equations and associated boundary conditions are derived by Hamilton's principle. A robust controller is designed using a linear matrix inequality (LMI) approach to the multiobjective synthesis. The design objectives are to achieve a mix of H$\sub$$\infty$/ performance and H$_2$ performance satisfying constraints on the closed-loop pole locations in the face of model uncertainties. Numerical examples are presented to demonstrate the effectiveness of LMI approach in damping out the multiple modes of vibration of the piezo/beam system.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.828-833
• /
• 2004

The performance of a mixed $H_{\infty}/H_2$ design with pole placement constraints based on robust vibration control for a piezo/beam system is investigated. The governing equation of motion for the piezo/beam system is derived by Hamilton's principle. The assumed mode method is used to discretize the governing equation into a set of ordinary differential equation. A robust controller is designed by $H_{\infty}/H_2$ feedback control law that satisfies additional constraints on the closed-loop pole location in the face of model uncertainties, which are derived for a general class of convex regions of the complex plane. These constraints are expressed in terms of linear matrix inequalities (LMIs) approach for the multiobjective synthesis. The validity and applicability of this approach for vibration suppressions of SMART structural systems are discussed by damping out the multiple vibrational modes of the piezo/beam system.