• Journal of Power Electronics
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• v.16 no.3
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• pp.1067-1075
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• 2016

This paper presents an adaptive observer-based approach to estimate voltage parameters, including frequency, amplitude, and phase angle, for single-phase power systems. In contrast to most existing estimation methods of grid voltage parameters, in this study, grid voltage is treated as a dynamic system related to an unknown grid frequency. Based on adaptive observer theory, a full-order adaptive observer is proposed to estimate voltage parameters. A Lyapunov function-based argument is employed to ensure that the proposed estimation method of voltage parameters has zero steady-state error, even when frequency varies or phase angle jumps significantly. Meanwhile, a reduced-order adaptive observer is designed as the simplified version of the proposed full-order observer. Compared with the frequency-adaptive virtual flux estimation, the proposed adaptive observers exhibit better dynamic response to track the actual grid voltage frequency, amplitude, and phase angle. Simulations and experiments have been conducted to validate the effectiveness of the proposed observers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.2
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• pp.10-18
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• 2003

This paper presents a method for designing guaranteed cost fuzzy filter with a desired H$_{\infty}$ disturbance rejection constraint of delayed fuzzy dynamic systems. This method not only guarantees an induced L$_2$ norm bound constraint on disturbance attenuation, but also minimizes an upper bound on a linear quadratic performance measure. A sufficient condition for the existence of guaranteed cost fuzzy filter with H$_{\infty}$ constraint is then presented in terms of linear matrix inequalities(LMIs). A simulation example is given to illustrate the design procedures and performances of the proposed methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.2
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• pp.67-74
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• 2007

Chattering phenomenon is still a large drawback of VSS. To overcome this problem, various approaches have been reported. A new notion of sliding sector has been proposed recently. Inside this sector, a kind of norm of the state decreases without control input. Therefore, so long as the state is constrained inside this sector, the norm of the state approaches to zero. The sliding sector theory is elementary study step and is studied about only linear systems. In this paper, new methods of the tracking control of unstable nonlinear systems using the sliding sector is proposed. This paper analyzes the stability, using Lyapunov function on the sliding sector. Through the computer simulations for an inverted pendulum system, it is verified that sliding sector control is capable to reduce the chattering.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.807-814
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• 2004

Chattering phenomenon is still a large drawback of VSS. To overcome this problem, various approaches have been reported. A new notion of sliding sector has been proposed recently. Inside this sector, a kind of norm of the state decreases without control input. Therefore, so long as the state is constrained inside this sector, the norm of the state approaches to zero. The sliding sector theory is elementary study step and is studied about only linear systems. In this paper, new methods of stabilizing unstable nonlinear systems using the sliding sector is proposed. This paper analyzes the stability, using Lyapunov function on the sliding sector. Through the computer simulations for an inverted pendulum system, it is verified that sliding sector control is capable to reduce the chattering.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.527-532
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• 2005

In this paper, we proposed an adaptive fuzzy sliding control for unknown nonlinear systems using estimation of bounds for approximation errors. Unknown nonlinearity of a system is approximated by the fuzzy logic system with a set of IF-THEN rules whose consequence parameters are adjusted on-line according to adaptive algorithms for the purpose of controlling the output of the nonlinear system to track a desired output. Also, using assumption that the approximation errors satisfy certain bounding conditions, we proposed the estimation algorithms of approximation errors by Lyapunov synthesis methods. The overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. The good performance of the proposed adaptive fuzzy sliding mode controller is verified through computer simulations on an inverted pendulum system.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.101-110
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• 2018

This paper introduces feedback linearization (FL) based adaptive sliding mode control (ASMC) effective against ground effects of the quadrotor UAV. The proposed control has the capability of estimation and effective rejection of those effects by adaptive mechanism, which resulting stable attitude and positioning of the quadrotor. As output variables of quadrotor, x-y-z position and yaw angle are chosen. Dynamic extension of the quadrotor dynamics is obtained for terms of roll and pitch control input to be appeared explicitly in x-y-z dynamics, and then linear feedback control including a ground effect is designed. A sliding mode control (SMC) is designed with a class of FL including higher derivative terms, sliding surfaces for which is designed as a class of integral type of resulting closed loop dynamics. The asymptotic stability of the overall system was assured, based on Lyapunov stability methods. It was evaluated through some simulation that attitude control capability is stable under excessive estimation error for unknown ground effect and initial attitude of roll, pitch, and yaw angle of $30^{\circ}$ in all. Effectiveness of the proposed method was shown for quadrotor system with ground effects.

• Journal of Power Electronics
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• v.19 no.3
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• Journal of Drive and Control
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• v.19 no.1
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• pp.51-61
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• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.

• Sleep Medicine and Psychophysiology
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• v.5 no.2
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• pp.185-193
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• 1998

Objectives : The hypothesis that the brain is a nonlinear dynamical system exhibiting deterministic chaos has offered new perspectives to the investigation of information processing in the brain of schizophrenic patients. It seemed worthwhile to estimate nonlinear measures of the electroencephalogram (EEG) in positive and negative schizophrenics, because nonlinear measures might serve as indicators of the specific brain function in schizophrenia according to specific psychopathologies. Method : Previous studies which estimated the chaoticity in the brain of schizophrenia with nonlinear methods recorded the EEGs at limited electrodes, so we tried to record EEGs from 16 channels for nonlinear analysis in 8 positive and 9 negative schizophrenics and 8 healthy control subjects. We employed a new method to calculate the nonlinear invariant measures. For limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Results : Our results showed that the patients with negative schizophrenia had lower the first positive Lyapunov exponents ($L_1$) than the positive schizophrnics and control subjects at $T_3$ lead. Positive symptoms were positively correlated with $L_1$ in $C_3,\;O_1$ leads, and negatively correlated with $C_4$ lead. Conclusion : These results suggest that if clinical variables such as psychopathology or neuroleptic medications would be well controlled, the nonlinear analysis of the EEGs in patients with schizophrenia seems to be a useful tool in analyzing EEG data to explore the neurodynamics.