• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.8
• /
• pp.779-784
• /
• 2008

This paper presents Kalman filter based system modeling algorithm for autonomous robot systems. State of the robot system is measured using embedded sensor systems and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state-space motion equation for unknown robot system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. To represent time-delay nature due to network media in system modeling, we construct an augmented state-space model which is mainly composed of original state and estimated parameter vectors. We conduct real-time experiment to test our proposed estimation algorithm where speed state of the constructed robot is used as system observation.

• Journal of Industrial Technology
• /
• v.43 no.1
• /
• pp.57-62
• /
• 2023

In this review, we introduce the non-parametric Bayesian filtering algorithm known as the point-mass filter (PMF) and discuss recent studies related to it. PMF realizes Bayesian filtering by placing a deterministic grid on the state space and calculating the probability density at each grid point. PMF is known for its robustness and high accuracy compared to other nonparametric Bayesian filtering algorithms due to its uniform sampling. However, a drawback of PMF is its inherently high computational complexity in the prediction phase. In this review, we aim to understand the principles of the PMF algorithm and the reasons for the high computational complexity, and summarize recent research efforts to overcome this challenge. We hope that this review contributes to encouraging the consideration of PMF applications for various systems.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.7
• /
• pp.87-94
• /
• 2019

Recently, the advance of accurate dynamic displacement measurement devices, such as GPS, computer vision, and optic laser sensor, has enhanced the structural monitoring technology. In this study, the dynamic displacement data was used to verify the applicability of the structural physical parameter estimation method through subspace system identification. The subspace system identification theory for estimating state-space model from measured data and physics-based interpretation for deriving the physical parameter of the estimated system are presented. Three-degree-freedom steel structures were fabricated for the experimental verification of the theory in this study. Laser displacement sensor and accelerometer were used to measure the displacement data of each floor and the acceleration data of the shaking table. Discrete state-space model generated from measured data was verified for precision. The discrete state-space model generated from the measured data extracted the floor stiffness of the building after accuracy verification. In addition, based on the story stiffness extracted from the state space model, five column stiffening and damage samples were set up to extract the change rate of story stiffness for each sample. As a result, in case of reinforcement and damage under the same condition, the stiffness change showed a high matching rate.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.6
• /
• pp.120-127
• /
• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Smart Structures and Systems
• /
• v.14 no.1
• /
• pp.17-37
• /
• 2014

In this study, four widely accepted and used variants of Evolution Strategies (ES) are adapted and applied to the output-error state-space identification problem. The selection of ES is justified by prior strong indication of superior performance to similar problems, over alternatives like Genetic Algorithms (GA) or Evolutionary Programming (EP). The ES variants that are being tested are (i) the (1+1)-ES, (ii) the $({\mu}/{\rho}+{\lambda})-{\sigma}$-SA-ES, (iii) the $({\mu}_I,{\lambda})-{\sigma}$-SA-ES, and (iv) the (${\mu}_w,{\lambda}$)-CMA-ES. The study is based on a six-degree-of-freedom (DOF) structural model of a shear building that is characterized by light damping (up to 5%). The envisaged analysis is taking place through Monte Carlo experiments under two different excitation types (stationary / non-stationary) and the applied ES are assessed in terms of (i) accurate modal parameters extraction, (ii) statistical consistency, (iii) performance under noise-corrupted data, and (iv) performance under non-stationary data. The results of this suggest that ES are indeed competitive alternatives in the non-linear state-space estimation problem and deserve further attention.

• International Journal of Aeronautical and Space Sciences
• /
• v.16 no.2
• /
• pp.223-246
• /
• 2015

Structural vibration control using a piezoelectric shunt is an established control technique. This technique involves connecting a piezoelectric patch, which is bonded onto or embedded into the vibrating structure, to an electric shunt circuit. Thus, vibration energy is converted into electrical energy and is dissipated through a network of electrical components. Different configurations of shunt have been researched, among which the negative capacitance-inductance shunt has gained prominence recently. It is basically an analog, active circuit consisting of operational amplifiers and passive elements to introduce real and imaginary impedance on the vibrating structure. The present study attempts to model the behavior of a negative capacitance-inductance shunt in terms of power output and efficiency using circuit modeling software. The shunt model is validated experimentally and is used to control the structural vibration of an aluminum beam, connected to a pair of piezoelectric patches, under broadband excitation. The model is also used to determine the optimal parameters of a negative capacitance-inductance shunt to increase the efficiency and predict the voltage output limit of op-amp against the supply voltage.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.54.1-54.1
• /
• 2019

We use iterative smoothing reconstruction method along with exploring in the parameter space of the light curves of the JLA supernova compilation (Joint Light-curve Analysis) to simultaneously reconstruct the expansion history of the universe as well as putting constrains on the light curve parameters without assuming any cosmological model. Our constraints on the light curve parameters of the JLA from our model-independent analysis seems to be closely in agreement with results assuming ΛCDM cosmology or using Chevallier-Polarski-Linder (CPL) parametrization for the equation of state of dark energy. This implies that there is no hidden significant feature in the data that could be neglected by cosmology model assumption. The reconstructed expansion history of the universe and properties of dark energy seems to be in good agreement with expectations of the standard ΛCDM model. Our results also indicate that the data allows a considerable flexibility for expansion history of the universe.

• Geomechanics and Engineering
• /
• v.23 no.4
• /
• pp.393-403
• /
• 2020

We design the Green-Naghdi model type III (GN-III) with widespread thermoelasticity for a thermoelectric half space using a memory-dependent derivative rule (MDD). Laplace transformations and state-space techniques are used in order to find the general solution for any set of limit conditions. A basic question of heat shock charging half space and a traction-free surface was added to the formulation in the present situation of a traveling heat source with consistent heating speed and ramp-type heating. The Laplace reverse transformations are numerically recorded. There are called the impacts of several calculations of the figure of the value, heat source spead, MDD parameters, magnetic number and the parameters of the ramping period.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.442-448
• /
• 2000

The conventional control of dynamic systems needs accurate mathematical modeling of control systems. But the modeling of dynamic systems require very complex computation process due to complex state equation and many control parameters. Accordingly this paper proposes a state space identification model of the dynamic system using neural networks. The Gauss-Newton method is used to train the proposed neural network and the effectiveness of proposed method is verified through the computer simulation of the Seesaw system identification problem.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.631-639
• /
• 2005

In this paper, we propose a constructive realization procedure for 2D systems which may lead to a Fornasini-Marchesini local state-space model with much lower order than the existing realization procedure given by Bisiacco et al. Nontrivial examples are illustrated and the conditions for minimal realization are also discussed.