• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.4
• /
• pp.185-193
• /
• 2022

This paper proposes an adaptive sliding mode control with radial basis function neural network(RBFNN) scheme to enhance the performance of position and attitude tracking control of quadrotor UAV. The RBFNN is utilized on the approximation of nonlinear function in the UAV dynmic model and the weights of the RBFNN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering problems, the sliding mode control term is adjusted by adaptive laws, which can enhance the robust performance of the system. The simulation results of the proposed control method confirm the effectiveness of the proposed controller which applied for a nonlinear quadrotor UAV is presented. Form the results, it's shown that the developed control system is achieved satisfactory control performance and robustness.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.1-12
• /
• 2022

In order to overcome the power fluctuation issues in photovoltaic (PV) smart grid-connected systems and the inverter nonlinearity model problem, an adaptive backstepping command-filter and a double second order generalized Integrators (DSOGI) controller are designed in order to tune the AC current and the DC-link voltage from the DC side. Firstly, we propose to present the filter mathematical model throughout the PV system, at that juncture the backstepping control law is applied in order to control it, Moreover the command filter is bounded to the controller aiming to exclude the backstepping controller differential increase. Additionally, The adaptive law uses Lyapunov stability criterion. Its task is to estimate the uncertain parameters in the smart grid-connected inverter. A DSOGI is added to stabilize the grid currents and eliminate undesirable harmonics meanwhile feeding maximum power generated from PV to the point of common coupling (PCC). Then, guaranteeing a dynamic effective response even under very unbalanced loads and/or intermittent climate changes. Finally, the simulation results will be established using MATLAB/SIMULINK proving that the presented approach can control surely the smart grid-connected system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.4
• /
• pp.366-372
• /
• 2002

EEG is the electrical signal, which is occurred during information processing in the brain. These EEG signal are measured by non-invasive method. EEG has many useful information for brain activity, but artifacts which are included in EEG prevents EEG analysis, so many efforts are devoted to remove these artifacts in EEG. However, this study is going to analysis the feature of the EEG mixed with artifacts in forward-looking way, by using this way, we have found the possibility that is actually applicable to system such as control system. We have made feature difference after the linear as well as nonlinear analysis regarding EEG including typical artifacts, eye-blinking, eye rolling, muscle, and so forth.

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

• Proceedings of the KIEE Conference
• /
• 2000.07c
• /
• pp.1850-1852
• /
• 2000

In this paper, new Partial Discharge (PD) detection technique using Pockels cell was proposed and considerable apparent chaotic characteristics were discussed. For this purpose, PD was generated from needle-plane electrode in air and detected by optical measuring system using Pockels cell, based on Mach-Zehnder interferometer, consisting of He-Ne laser, single mode optical fiber, 50/50 beam splitter and photo detector. A qualitative analysis was carried out by drawing Return map for the normalized time series of the detected PD signals. The results are as follows:(a) Fixed points, between 0.7 and 1.0, are appeared clearly in the right upper area of the return map as the increase in the number of obtained data.(b) Considerable periodicity have been remarked even though exact period and length can not be determined.(c) The self-similarity can be also observed inasmuch as the late paths do not follow the previous ones. Accordingly, exact quantitative analysis such as embedding dimension, fractal dimension, and Lyapunov exponents should be carried out for deducing the quantitative properties regarding PD phenomena.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.3
• /
• pp.1060-1071
• /
• 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 Institute of Control, Robotics and Systems
• /
• v.11 no.9
• /
• pp.781-788
• /
• 2005

This paper proposes an identification method using a self recurrent wavelet neural network (SRWNN) for dynamic systems. The architecture of the proposed SRWNN is a modified model of the wavelet neural network (WNN). But, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. Thus, in the proposed identification architecture, the SRWNN is used for identifying nonlinear dynamic systems. The gradient descent method with adaptive teaming rates (ALRs) is applied to 1.am the parameters of the SRWNN identifier (SRWNNI). The ALRs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of an SRWNNI. Finally, through computer simulations, we demonstrate the effectiveness of the proposed SRWNNI.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.3
• /
• pp.183-189
• /
• 2013

This paper presents a stabilization method for NCS (Networked Control Systems) with two additive time-varying delays. Each time delay component between the plant and the controller has different characteristics depending on communication network, and has the upper and lower bounds. The time delay occurring from the controller to the plant has an effect on the time delay occurring from the plant to the controller, and the relationship between two delays is taken into account on the stability analysis. Based on the two additive delay components and the bound conditions, the appropriate Lyapunov-Krasovskii functional and the LMI (Linear Matrix Inequality) method derive the stability condition and the $H_{\infty}$ norm constraint for time-varying delayed NCS. Simulation results are finally given to demonstrate the effectiveness of the proposed method.

• Wind and Structures
• /
• v.6 no.2
• /
• pp.151-164
• /
• 2003

On the basis of the first Lyapunov stability theory, this paper develops a dynamic stability criterion for elastic structural systems under arbitrary dynamic loads, and shows the stability criterion using energy variation. After the dynamic stability criterion is validated through a classic example, it is used for the dynamic stability investigation of practical guyed masts under random wind loads. The criterion is reliable, simple and of advantage for structures with large number of elements and nodes. The slack guys and internal resonance between guys and mast are two main factors which induces the dynamic instability of guyed masts, at the same time, some dynamic stability characteristics of guyed masts are found.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.5
• /
• pp.526-532
• /
• 2014

In this paper, we present and analyze a LQ (Linear Quadratic) inverse optimal state-consensus protocol for continuous-time multi-agent systems with undirected graph topology. By Lyapunov analysis of the state-consensus error dynamics, we show the sufficient conditions on the algebraic connectivity of the graph to guarantee LQ inverse optimality and closed-loop stability. A more relaxed stability condition is also provided in terms of the algebraic connectivity. Finally, a formation control protocol for multiple mobile robots is proposed based on the target LQ inverse optimal consensus protocol, and the simulation results are provided to verify the performance of the proposed LQ inverse formation control method.