• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1244-1254
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• 2015

This paper proposes a new method for tuning a linear quadratic - proportional integral derivative controller for second order systems to simultaneously meet the time and frequency domain design specifications. The suitable loop-shape of the controlled system and the desired step response are considered as specifications in the time and frequency domains, respectively. The weighting factors, Q and R of the LQ controller are determined by the algebraic Riccati equation with respect to the limiting behavior and target function matching. Numerical examples show the effectiveness of the proposed LQ-PID tuning method

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.118-126
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• 2002

AVR parameter tuning for voltage control of generators has generally been done with the off-line open-circuit model of the synchronous generator. When the generator is connected on-line and operating with load the AVR operates in an entirely different environment from the open-circuit conditions. This paper describes a new method for AVR parameter tuning for on line conditions using SQP(Sequential Quadratic Programming) meshed with frequency response characteristics of linearized on-line system model. As the proposed method uses the un - line system model the tuned parameter sets show more optimal behavior in the on-line operating conditions. furthermore, as this method considers the performance indices that are needed for stable operation as constraints, AVR parameter sets that are tuned by this method could guarantee the stable performance, too.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.795-797
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• 1995

In this paper, we present a simple auto-tuning PID controller using genetic algorithms. The basic idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a single parameter ${\alpha}$, then to use GA to select optimal tuning parameter. Also, simple rule mechanisms make the controller adapt against large variations in parametric and dynamics uncertainties in the plant. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with long delay-time and nonminimum phase systems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.394-400
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• 2023

In this study, the tuning phenomena, gas storage capacity, and thermal expansion behaviors of binary (cyclopentylamine + CH₄) and (cyclopropylamine + CH₄) clathrate hydrates were investigated for the potential applications of clathrate hydrates to gas storage. To understand the tuning behaviors of binary (cyclopentylamine + CH₄) and (cyclopropylamine + CH₄) clathrate hydrates, ¹³C solid-state NMR spectroscopy was used, and the results confirmed that maximum tuning factors for the binary (cyclopentylamine + CH₄) and (cyclopropylamine + CH₄) clathrate hydrates were achieved at 0.5 mol% and 1.0 mol% of guest concentration, respectively. The gas storage capacity of binary (cyclopentylamine + CH₄) and (cyclopropylamine + CH₄) clathrate hydrates were also checked, and the results showed the CH₄ capacity of our hydrate systems was superior to that of binary (tetrahydrofuran + CH₄) and (cyclopentane + CH₄) clathrate hydrates. The synchrotron diffraction patterns of these hydrates collected at 100, 150, 200, and 250 K confirmed the formation of a cubic Fd-3m hydrate. In addition, the lattice constant of clathrate hydrates with cyclopentylamine and methane were larger than that with cyclopropylamine and methane due to the effects of molecular size and shape.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.4
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• pp.28-37
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• 2002

Acoustic behavior in combustion chamber with acoustoc cavity is numerically investigated by adopting linear acoustic analysis. Helmholtz-type resonator is employed as a cavity model to suppress acoustic instability passively. The tuning frequency of acoustic cavity is adjusted by varying the sound speed in acoustic cavity. Through harmonic analysis, acoustic pressure responses of chamber to acoustic oscillating excitation are shown and the resonant acoustic modes are identified. Acoustic damping effect of acoustic cavity is quantified by damping factor. As the tuning frequency approaches the target frequency of the resonant mode to be suppressed, mode split from the original resonant mode to lower and upper modes appears and thereby damping effect is degraded significantly. Considering mode split and damping effect as a function of tuning frequency, it is desirable to make acoustic cavity tuned to maximum frequency of those of the possible splitted upper modes.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.12
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• pp.55-61
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• 2009

Machining systems have been evolved to produce more detailed products of high added value. This has been possible, in large part, due to the development of highly accurate multi-axis CNC machine tools. The conventional CNC of machine tools has individual axis controllers to maximize tracking performance. On the other hand, cross-coupling controllers can be integrated into the conventional CNC to enhance contouring performance. For this multi-axis cross-coupling control system, it is necessary to automatically adjust the controller gains depending on operating conditions and/or other external conditions from an optimization perspective. This paper proposes automatic modeling of feed drive systems that minimizes the difference in behavior between the system model and the actual system. Based on the modeling, an integrated auto-tuning method is also proposed to improve both tracking and contouring accuracy of a 3-axis cross-coupling control system as well as users' convenience. The proposed methods are evaluated by both simulation and experiments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.1-8
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• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• Journal of Power Electronics
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• v.14 no.5
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• pp.980-988
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• 2014

This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC) term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate the nonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore, unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. As a result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully described based on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) scheme is also tested under the same conditions as the proposed control method. It can be seen from the experimental results that the proposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smaller steady-state error) than the conventional SMC method.