Sin Ho Lee

Sliding Mode Control Based on Self-Recurrent Wavelet Neural Network for Five-link Biped Robot

In this paper, we propose the intelligent control of biped robot system with unknown model uncertainty. In our proposed control system, we employ the sliding mode control (SMC) for stable walking control of biped robot and the error compensation controller for the approximation error of self-recurrent wavelet neural network (SRWNN) which is used to estimate unknown model uncertainty of the biped robot system and nonlinear system parameters. Also, the adaptive laws for all weights of SRWNN are induced from the Lyapunov stability theorem, which are used to guarantee the stability of control system. Finally, we carry out computer simulations based on the 5-link biped robot model for the effectiveness of the proposed control system

Finite time control of nonlinear underactuated systems using terminal sliding surface

In this paper, we propose a hierarchical terminal sliding mode control approach for nonlinear underactuated systems which cna drive the error to zero in a finite time. Here, the controller has the double layer structure because the system is divided into two hierarchical subsystems. In the first layer, the terminal sliding surfaces are hierarchically designed for each subsystem, and in the second layer, the whole sliding surface i designed as the linear combination of terminal sliding surfaces. The asymptotic stability of the system is verified by Lyapunov analysis because the controller is based on terminal sliding sufrace. Finally, we carry out simulation on a representative underactuated systems such as the overhead crane system, to illustrate the effectiveness of the proposed control scheme.

Terminal sliding mode control of nonlinear chaotic systems using self-recurrent wavelet neural network

In this paper, we design a self-recurrent wavelet neural network (SRWNN) based terminal sliding mode controller for nonlinear chaotic systems with uncertainties. The nonlinear chaotic systems are decomposed into a sum of a nominal nonlinear part and uncertainty term. The terminal sliding mode control (TSMC) method which has been used to control the system robustly can drive the tracking errors to zero in a finite time. In addition, the TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with the classical sliding mode control (CSMC). For the control of nonlinear chaotic system with various uncertainties, we employ the SRWNN which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations on two nonlinear chaotic systems such as Duffing system and Lorenz system to illustrate the effectiveness of the proposed control.

Blind spot reduction in wavelet transform-based time–frequency domain reflectometry using Gaussian chirp as mother wavelet

In this study, the authors propose a blind spot reduction method in wavelet transform-based time–frequency domain reflectometry (WTFDR) by using the Gaussian chirp as the mother wavelet. The blind spot is one of the intrinsic weak points in reflectometry and it means the overlapping ranges of the reference and the reflected signals when the fault is generated at a close distance. Owing to the blind spot, it is difficult to localise the close range fault. Thus, many researchers study the blind spot which is generated in various cables such as electric cable in flight, network cable and power cable. In this study, two methods are used to reduce the blind spot. Firstly, by using the linearity of a complex wavelet transform, the overlapped reference signal at the measured signal is separated and the blind spot is reduced by obtaining the difference of the moduli of the wavelet coefficients for the reference and the reflected signals. Secondly, by using the Gaussian chirp as the mother wavelet, which is designed by considering the characteristics of the cable, the wavelet analysis and the resolution of the WTFDR are improved. Finally, the computer simulations and the real experiments are performed to confirm the effectiveness and the accuracy of the proposed method.

Wavelet-transform-based time?frequency domain reflectometry for reduction of blind spot

In this paper, wavelet-transform-based time–frequency domain reflectometry (WTFDR) is proposed to reduce the blind spot in reflectometry. TFDR has a blind spot problem when the time delay between the reference signal and the reflected signal is short enough compared with the time duration of the reference signal. To solve the blind spot problem, the wavelet transform (WT) is used because the WT has linearity. Using the characteristics of the WT, the overlapped reference signal at the measured signal can be separated and the blind spot is reduced by obtaining the difference of the wavelet coefficients for the reference and reflected signals. In the proposed method, the complex wavelet is utilized as a mother wavelet because the reference signal in WTFDR has a complex form. Finally, the computer simulations and the real experiments are carried out to confirm the effectiveness and accuracy of the proposed method.

Non-invasive monitoring of underground power cables using Gaussian-enveloped chirp reflectometry

In this paper, we introduce non-invasive Gaussian-enveloped linear chirp (GELC) reflectometry for the diagnosis of live underground power cables. The GELC reflectometry system transmits the incident signal to live underground power cables via an inductive coupler. To improve the spatial resolution of the GELC reflectometry, we used the multiple signal classification method, which is a super-resolution method. An equalizer, which is based on Wiener filtering, is used to compensate for the signal distortion due to the propagation characteristics of underground power cables and inductive couplers. The proposed method makes it possible to detect impedance discontinuities in live underground power cables with high spatial resolution. Experiments to find the impedance discontinuity in a live underground power cable were conducted to verify the performance of the proposed method.

Measurement of load impedance in power cables using wavelet-transform-based time?frequency domain reflectometry

In this paper, wavelet-transform-based time–frequency domain reflectometry (WTFDR) is proposed for load impedance measurement. In order to measure the load impedance, the energy of the measured signal in the time–frequency domain, the phase difference between the reference signal and the reflected signal, the characteristic impedance, and the attenuation factor of the measured cable must all be known. Since the complex wavelet transform is composed of real and imaginary parts, the phase difference is easily obtained using the ratio of the real coefficient to the imaginary coefficient. In addition, the wavelet energy denotes the sum of the square of the modulus of the wavelet transform and describes the energy of the measured signal in the time and frequency domains. To accurately determine the characteristic impedance and attenuation factors, the power cable should be estimated as a coaxial cable. Using WTFDR with the complex mother wavelet and the estimated power cable, the load impedance can be obtained simply and accurately. Finally, real experiments for the evaluation of various load impedances are carried out to confirm the effectiveness and accuracy of the proposed method compared to the conventional time–frequency domain reflectometry.

Multi-Impedance Change Localization of the On-Voltage Power Cable Using Wavelet Transform Based Time-Frequency Domain Reflectometry

In this paper, we propose a multi-impedance changes localization method of on-voltage underground power cable using the wavelet transform based time-frequency domain reflectometry (WTFDR). To localize the impedance change in on-voltage power cable, the TFDR is the most suitable among reflectometries because the inductive coupler is used to inject the reference signal to the live cable. At this time, the actual on-voltage power cable has multi-impedance changes such as the automatic section switches and the auto load transfer switches. However, when the multi-impedance changes are generated in the close range, the conventional TFDR has the cross term interference problem because of the nonlinear characteristics of the Wigner-Ville distribution. To solve the problem, the wavelet transform (WT) is used because it has the linearity. That is, using WTFDR, the cross term interference is not generated in multi-impedance changes due to the linearity of the WT. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for the on-voltage underground power cable.

Wavelet Transform Based Time-Frequency Domain Reflectometry for Underground Power Cable

In this paper, we develope a wavelet transform based time-frequency domain reflectometry (WTFDR) for the fault localization of underground power cable. The conventional TFDR (CTFDR) is more accurate than other reflectometries to localize the cable fault. However, the CTFDR has some weak points such as long computation time and hard implementation because of the nonlinearity of the Wigner-Ville distribution used in the CTFDR. To solve the problem, we use the complex wavelet transform (CWT) because the CWT has the linearity and the reference signal in the TFDR has a complex form. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for various fault types of the underground power cable.