Kwanho You

The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic

This study discusses the quantised linear quadratic Gaussian (LQG) control problem for linear stochastic systems. A symmetric channel that connects the sensor and the controller is considered. Given a quantiser that is applied on the most recent innovation of the measurement, it is shown that the well-known separation principle remains valid. Based on a quantised innovations Kalman filter, a suboptimal LQG controller is given in terms of the solutions of two Riccati difference equations associat

In this study, a sliding-mode controller is designed using an adaptive reaching law with a super-twisting algorithm. A dynamic model of a drone is designed with a quadrotor that has four motors and considers disturbances and model uncertainties. Given that the drone operates as an under-actuated system, its flight stability and maneuverability are influenced by the discontinuous signal produced by the reaching law of the sliding-mode control. Therefore, this study aims to improve the sliding-mod

We propose a control method wherein the estimated angles converge to the desired value for quadrotor attitude stabilization and position tracking. To improve the performance of a quadrotor system, the unmeasured states of the quadrotor are estimated using a sliding mode observer (SMO). We set up a quadrotor dynamic model and augment the quadrotor dynamics by an SMO. We also derive the control inputs by sliding mode control (SMC) and calculate the desired angle of the quadrotor to reach the targe

In the paper, a robust optimal control algorithm is presented for a nonlinear second order systems with model uncertainty. To do so, the bang-bang principle, singular control and sliding-mode control are combined. The state space is divided into three regions to implement the control algorithm. In each region, the appropriate linear and nonlinear feedback control law is used satisfying the dynamic system equations. The algorithm blends smoothly not to give stress on the system. Comparison with p

Sliding mode control and disturbance compensation techniques are applied to a nonlinear speed control algorithm for a permanent magnet synchronous motor (PMSM). Optimizing the speed control performance of PMSM systems with various disturbances and uncertainties is challenging. To achieve a satisfactory performance, a sliding mode control method based on the super-twisting algorithm reaching law (STRL) is presented. STRL can adapt dynamically to the variations of a controlled system. The STRL mai

In this paper, we suggest a seismic signal measurement system that uses a laser interferometer. The heterodyne laser interferometer is used as a seismometer due to its high accuracy and robustness. Seismic data measured by the laser interferometer is used to analyze crucial earthquake characteristics. To measure P-S time more precisely, the short time Fourier transform and instantaneous frequency estimation methods are applied to the intensity signal ( I y ) of the laser interferometer. To estim

Among various localization methods, a localization method that uses a radio frequency signal-based wireless sensor network has been widely applied due to its robustness against noise factors and few limits on installation location. In this paper, we focus on an iterative localization scheme for a mobile with a limited number of time difference of arrival (TDOA) and angle of arrival (AOA) data measured from base stations. To acquire the optimal location of a mobile, we propose a recursive solutio

The permanent magnet synchronous motor (PMSM) has been of interest to eco-friendly industries on account of its advantages such as high performance, efficiency, and precision control. However, perturbations due to PMSM parameter uncertainty, load torque, and external disturbance interfere with the construction of PMSM precision control systems. Therefore, a robust control system is needed to avoid unnecessary system movement caused by perturbations. In this paper, sliding mode control (SMC) is a

Although sliding mode control (SMC) provides powerful control performance, it exhibits chattering phenomena that can lead to operational issues in quadrotor systems when certain thresholds are reached. To address this limitation, this study introduces second-order sliding mode control (SOSMC), which significantly reduces chattering. Furthermore, a modified version of the SOSMC, called advanced second-order sliding mode control (ASOSMC), is proposed by incorporating an additional term to enhance

In this paper, we design a robust servo controller for hard disk drives in track-following mode. To follow the target track center accurately under the effects of system uncertainties and various external disturbances, we propose a robust sliding-mode control with a new switching surface. To reduce the chattering effect, the augmented system adopts two auxiliary states (i.e., state estimates and control input differential), as suggested in this paper. Simulation results show that the chattering

It is known that the most stressful bounded (time maximum) disturbance for stabilized linear systems is of bang-bang type. This bang-bang disturbance can often be implemented with a switch set of current states for second-order systems. The isochrones, as the level sets, determine the value of the disturbance index in a state space setting. We suggest an efficient way to construct the time maximum disturbance from the information of the isochronal wave front using it in a model predictive scheme

The discrimination between earthquakes and artificial explosions is a significant issue in seismic analysis to efficiently prevent and respond to seismic events. However, the discrimination of seismic events is challenging due to the low incidence rate. Moreover, the similarity between earthquakes and artificial explosions with a local magnitude derives a nonlinear data distribution. To improve the discrimination accuracy, this paper proposes machine-learning-based seismic discrimination methods