• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.12
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• pp.9-20
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• 2012

This paper proposes the efficiency optimization control of interior permanent magnet synchronous motor(IPMSM) drive using series connected-fuzzy neural network PI(SC-FNPI) controller. The PI controller is generally used to control IPMSM drive in industrial field. However, the PI controller has problem which is falling control performance about parameter variation such as command speed, load torque and inertia due to fixed gain of PI controller. Therefore, to improve performance of PI controller, this paper proposes SC-FNPI controller adjusted input of PI controller by FNN controller according to operating conditions. Also, this paper proposes efficiency optimization control which is improving efficiency with minimize loss. The SC-FNPI controller proposed in this paper is compared control performance with conventional FNN and PI controller about command speed, load torque and inertia variation. And the efficiency optimization control is compared with $i_d=0$ control about loss and efficiency. The SC-FNPI controller proposed in this paper shows more excellent control performance for rising time, overshoot and steady-state error. Also efficiency optimization control is increased efficiency by reducing loss.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.298-306
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• 2006

In our previous paper, we developed a robust saturation controller for the linear time-invariant (LTI) system involving both actuator's saturation and structured real parameter uncertainties. This controller can only guarantee the closed-loop robust stability of the system in the presence of actuator's saturation. But we cannot analytically make any comment on control performance of this controller. In this paper, we suggest a method to use linear matrix inequality (LMI) optimization problem which can analytically explain control performance of this robust saturation controller only in nominal system. The availability of design method using LMI optimization problem for this robust saturation controller is verified through a numerical example for the building with an active mass damper (AMD) system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.701-708
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• 2017

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2069-2074
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• 2016

This paper presents a methodology based on evolutionary optimization for simultaneously optimizing design parameters of controller and components of plant. Genetic programming(GP) based bond graph model generation is adopted to open-ended search for the plant. Also GP is applied to represent the controller with a unified method. The formulations of simultaneous plant-controller design optimization problem and the description of solution techniques based on bond graph are derived. A feasible solutions for a plant/controller design using the simultaneous optimization methodology is illustrated.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2322-2329
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• 2008

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of particle swarm optimization(PSO) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling facrors) of each fuzzy controller using PSO. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on PSO, is presented in comparison with the conventional PD cascade controller based on serial genetic alogritms.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1863-1871
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• 2016

This paper presents an application of fractional order controller for the control of multi input multi output twin rotor aerodynamic system. Dynamics of the considered system are highly nonlinear and there exists a significant cross-coupling between the horizontal and vertical axes (pitch & yaw). In this paper, a fractional order model of twin rotor aerodynamic system is identified using input output data from nonlinear system. Based upon identified fractional order model, a fractional order PID controller is designed to control the angular position of level bar of twin rotor aerodynamic system. The parameters of controller are tuned using Nelder-Mead optimization and compared with particle swarm optimization techniques. Simulation results on the nonlinear model show a significant improvement in the performance of fractional order PID controller as compared to a classical PID controller.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.157-165
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• 1995

The new formulation of designing the two degree of freedom(TDF) robust controller is proposed using $H_{\infty}$optimization and model matching method. In this formulation the feedback controller and feedforward controller are designed in a single step using $H_{\infty}$optimization procedure. Roughly speaking, the feedback controller is designed to meet robust stability and disturbance rejection specifications, while the feedforward controller is used to improve the robust model matching properties of the closed loop system. The proposed formulation will be illustrated and evaluated on a seeker scan-loop. And the performances of TDF robust controller are compared with those of the $H_{\infty}$ controller designed using Loop Shaping Design Procedure proposed by McFarlane and Glover.lover.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.551-559
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• 2015

This paper's focus is in reducing the torque ripple and increasing the average torque by optimizing switching angles of 8/6 switched reluctance motor while implementing a robust speed controller in the outer loop. The mathematical model of the machine is developed and it is simulated using MATLAB/Simulink. An objective function and constraints are formulated and Optimum turn-on and turn-off angles are determined using Particle swarm optimization and Genetic Algorithm techniques. The novelty of this paper lies in implementing sliding mode speed controller with optimized angles. The results from both the optimization techniques are then compared with initial angles with one of them clearly being the better option. Speed response is compared with PID controller.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.781-797
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• 2020

In recent years, there is an increasing interest to optimize the fuzzy logic controller with different methods. This paper focuses on the optimization of a fuzzy logic controller applied to a seismically excited nonlinear building. In most cases, this problem is formulated based on the linear behavior of the structure, however in this paper, four sets of objective functions are considered with respect to the nonlinear responses of the structure as the peak interstory drift ratio, the peak level acceleration, the ductility factor and the maximum control force. The Improved Charged System Search is used to optimize the membership functions and the rule base of the fuzzy controller. The obtained results of the optimized and the non-optimized fuzzy controllers are compared to the uncontrolled responses of the structure. Also, the performance of the utilized method is compared with various classical and advanced optimization algorithms.