• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.7
• /
• pp.637-648
• /
• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.6
• /
• pp.459-464
• /
• 2001

This paper discusses a stochastic stabilization of Takagi-Sugeno(TS) fuzzy system with Markovian input delay. The finite Markovian process is adopted to model the input delary of the overall control system. It is assumed that the zero and hold devices are used for control input. The continuous-time TS fuzzy system with the Markovian input delay is discretized for easy handling delay, according, the discretized TS fuzzy system is represented by a discrete-time TS fuzzy system with jumping parameters. The stochastic stabilizibility of the jump TS fuzzy system is derived and formulated in terms of linear matrix inequalities (LNIS)

• International Journal of Control, Automation, and Systems
• /
• v.6 no.4
• /
• pp.506-514
• /
• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• Earthquakes and Structures
• /
• v.10 no.6
• /
• pp.1451-1465
• /
• 2016

The effectiveness of base isolation (BI) systems for mitigation of seismic vibration of bridges have been extensively studied in the past. It is well established in those studies that the performance of BI system is largely dependent on the characteristics of isolator yield strength. For optimum design of such systems, normally a standard nonlinear optimization problem is formulated to minimize the maximum response of the structure, referred as Stochastic Structural Optimization (SSO). The SSO of BI system is usually performed with reference to a problem of unconstrained optimization without imposing any restriction on the maximum isolator displacement. In this regard it is important to note that the isolator displacement should not be arbitrarily large to fulfil the serviceability requirements and to avoid the possibility of pounding to the adjacent units. The present study is intended to incorporate the effect of excessive isolator displacement in optimizing BI system to control seismic vibration effect of bridges. In doing so, the necessary stochastic response of the isolated bridge needs to be optimized is obtained in the framework of statistical linearization of the related nonlinear random vibration problem. A simply supported bridge is taken up to elucidate the effect of constraint condition on optimum design and overall performance of the isolated bridge compared to that of obtained by the conventional unconstrained optimization approach.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5218-5233
• /
• 2018

The coexisting wireless body area networks (WBAN) is a very challenging issue because of strong inter-networks interference, which seriously affects energy consumption and spectrum utilization ratio. In this paper, we study a power control strategy with nearest neighbor nodes distribution for coexisting WBAN based on stochastic geometry. Using homogeneous Poisson point processes (PPP) model, the relationship between the transmission power and the networks distribution is analytically derived to reduce interference to other devices. The goal of this paper is to increase the transmission success probability and throughput through power control strategy. In addition, we evaluate the area spectral efficiency simultaneously active WBAN in the same channel. Finally, extensive simulations are conducted to evaluate the power control algorithm.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.18 no.3
• /
• pp.1-12
• /
• 1993

Several alternative manufacturing control strategies are under study in the literature. They are, specifically, push system, pull system, conwip system, and as a special case, infinite buffer system. We focus on modeling, comparison analysis and design of these systems. The event epoch sequences of each system are generated which also enable us to compare their performance. Then the stochastic monotonicity of these enent epoch sequences in several important design parameters are established through the structure of the generalized semi-Markov schemes on which they are based. Finally, we solve the stochastic optimization problem which minimizes these event epochs. Our results supplement the applicability of some previously known results in the literature.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.312-317
• /
• 1992

This paper proposes a new framework of an autonomous and distributed flexible manufacturing system - Multi Client Robot Groups(MCR) - and describes a stochastic learning scheme applied to managerial problems of the system. The MCR is composed of groups of manufacturing robots, named Client Robots (CRs), which are capable of both versatility and independence in their performances. The MCR is expected to have high performance because the MCR can perform concurrent and corporative processing. However, the system performance is determined by the organizations of the CR groups. Therefore the treatment of the managerial problems and organizations of the system are important problems. In this paper, it is assumed that CR groups being able to processing tasks are selected stochastically based on the strengths of the robot groups. The learning scheme adjusting the strength is introduced to organize the groups in the system and control the each performance of the groups according to the total system performance. Finally, some experimental results of the learning scheme are shown.

• Journal of applied mathematics & informatics
• /
• v.42 no.2
• /
• pp.223-235
• /
• 2024

In this paper, our primary focus revolves around the examination of a set of fractional stochastic models. Through our investigation, we can establish the presence of a solution and its distinctiveness. Additionally, we employ a moment-based algorithm to estimate the coefficients within these models and provide evidence that these estimations maintain their asymptotic characteristics. To support this claim, we conduct experimental studies using simulations and numerical examples.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.73-77
• /
• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.96.3-96
• /
• 2001

In this paper the self-organized and fuzzy inference used stochastic gradient ascent method is proposed. Fuzzy rule and fuzzy set increase as occasion demands autonomously according to the observation information. And two rules(or two fuzzy sets)becoming to be similar each other as progress of learning are unified. This unification causes the reduction of a number of parameters and learning time. Using fuzzy inference and making a rule with an appropriate state division, our proposed method makes it possible to construct a robust reinforcement learning system.