• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.34-40
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• 2005

This paper presents a frequency transfer function synthesis for simplifying a high-order model with time delay to a low-order model. A model reduction is based on minimizing the error function weighted by the numerator polynomial of reduced systems. The proposed method provides better low frequency fit and a computer aided algorithm. And in this paper, we present a design method of PID controller for achieving the desired specifications via the reduced model. The proposed method identifies the parameter vector of PID controller from a linear system that develops from rearranging the two dimensional input matrices and output vectors obtained from the frequency bounds.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.3
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• pp.313-319
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• 2017

This study presents a genetic algorithm (GA) to design a PID controller systematically for an inchworm operated by piezoelectric actuators. The performance index considering overshoot and settling time is adopted to search an optimal PID gain using GA. The piezoelectric actuator shows nonlinear characteristics including hysteresis and residual displacement. The PID feedback system combined with an integrator is used to improve the ability of tracking the complex input signals and suppressing the steady state error. The PID controller tuned by GA can track the various motion contours effectively. However, the PID controller shows an improper residual vibration under the application of high-frequency square input. The input shaper combined with the feedback system can overcome this limitation of the PID controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.211-218
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• 1983

In this paper, an attempt is made to match the continuous state trajectory of the digital control system with that of its continuous data model. Matching the state trajectories instead of the output responses assures that the performances of the internal variables of the plant as well as the output variables are preserved in the discretization. The mathematical tool used in this research is an extended maximum principle of the Pontryagin type, which enables one to synthesize a staircase type of optimal control signals, such as the output signal of a zero-order hold asociated with a digital controller. A general mathematical expression of the digital controller which may be used to replace the analog controller of a general system while preserving as mauch as possible the performance characteristics of the original continuous-data control system is derived in this paper.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.940-947
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• 2022

Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional-integral-derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1327-1329
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• 2005

This paper presents a gam optimization method using genetic algorithm(GA) for a magnetic levitation(Maglev) controller. GA uses the integral of square error(ISE) as performance index. The plant dynamics are described and modelled by mathematical equations. Also, the system apparatus for the Maglev system are described. Using the derived model, to optimize the feedback gains of conventional state feedback controller(SFC), GA is simulated with SIMULINK model. finally, using the optimized feedback gains, SFC is applied to the Maglev system. From the results, we can see that GA can give a solution for the better control performance for the Maglev system.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.2
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• pp.19-25
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• 2005

In this paper, the feedforward control with least mean square (LMS) adaptive algorithm is proposed and examined to reduce rotating error by runout of an active magnetic bearing system. Using eddy-current type gap sensors for control, the electrical runout caused by non-uniform material properties of sensor target produces rotational error amplified in feedback control loop, so this runout should be eliminated to increase rotating accuracy. The adaptive feedforward controller is designed and examined its tracking performances and stability numerically with established frequency response function. The designed feedforward controller was applied to a grinding spindle system which is manufactured with a 5.5 kW internal motor and 5-axis active magnetic bearing system including 5 eddy current gap sensors which have approximately 15∼30㎛ of electrical runout. According to the experimental results, the error signal in radial bearings is reduced to less than 5 ,Urn when it is rotating up to 50,000 rpm due to applying the feedforward control for first order harmonic frequency, and corresponding vibration of the spindle is also removed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.395-401
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• 1999

In this paper, a temperature controller for a semiconductor test handler is proposed. First, a handware system for identification and control is established using RTD sensors, an A/D converter, solid state relays, a heater, and a computer system. Second, using ARMAX model and least square method, a chamber model for the design of a controller is identified through experiments. The identified model is verified to describe the real plant very well in the sense that it shows very similar input-output responses to those of the real system. With the identified model an LQG controller is designed. Frequency response of the designed controller shows that it has 15 dB of gainmargin and (-50˚, +50˚) of phase margin. Experiment with a real test handler demonstrates a good performance in the sense that its overshoot and steady state error are smaller and response time is faster, compared with those of a conventional PID controller.