• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1731_1732
• /
• 2009

This paper presents a stability analysis of networked control systems with packet dropouts. The packet dropouts are modeled as a linear function of the stochastic variable satisfying Bernoulli random binary distribution and weighted moving average (WMA). The observer based controller scheme is designed to exponentially mean square stabilize the NCS. Simulation results is provided to show the applicability of the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.17 no.32
• /
• pp.145-154
• /
• 1994

In this paper, monitoring processor in a circuit switched network is considered. Monitoring processor monitors communication links, and offers a grade of service in each link to controller. Such an information is useful for an effective maintenance of system. Two links with nonsymmetric system Parameters are considered. each link is assumed independent M/M/1/1 type. The Markov process is introduced to compute busy and idle portions of monitoring processor and monitored rate of each link. Inter-idle times and inter-monitoring times of monitoring processor between two links are respectively computed. A recursive formula is introduced to make computational procedure rigorous.

• Advances in aircraft and spacecraft science
• /
• v.3 no.2
• /
• pp.211-224
• /
• 2016

The present study aims to investigate the shimmy stability behavior of a single wheeled nose landing gear system. The system is supposed to be equipped with an electromechanical actuator capable to control the shimmy vibrations. A Proportional-Integrative-Derivative (PID) controller, tuned by using the Particle Swarm Optimization (PSO) procedure, is here proposed to actively damp the shimmy vibration. Time-history results for some test cases are reported and commented. Stochastic analysis is last presented to assess the robustness of the control system.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.236-243
• /
• 2005

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.686-689
• /
• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.643-653
• /
• 2017

The paper presents controller optimization algorithm for a 12-pulse voltage source converter (VSC) based high voltage direct current (HVDC) system. To get an optimum algorithm, three methods namely conventional-Zeigler-Nichols, linear-golden section search (GSS) and stochastic-particle swarm optimization (PSO) are applied to control of 12 pulse VSC based HVDC system and simulation results are presented to show the best among the three. The performance results are obtained under various dynamic conditions such as load perturbation, non-linear load condition, and voltage sag, tapped load fault at points-of-common coupling (PCC) and single-line-to ground (SLG) fault at input AC mains. The conventional GSS and PSO algorithm are modified to enhance their performances under dynamic conditions. The results of this study show that modified particle swarm optimization provides the best results in terms of quick response to the dynamic conditions as compared to other optimization methods.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.286-295
• /
• 2007

This paper presents an intelligent PID control for stochastic systems with nonstationary nature. We optimally determine parameters of a PID controller through learning algorithm and propose an online PID control to compensate system errors possibly occurred in realtime implementations. A dynamic Bayesian network (DBN) model for system errors is additionally explored for making decision about whether an online control is carried out or not in practice. We apply our control approach to traffic control of Transmission Control Protocol (TCP) networks and demonstrate its superior performance comparing to a fixed PID from computer simulations.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.633-635
• /
• 1999

We design to the temperature control system based on Receding horizon control(RHC) with a terminal output weighting for stochastic state model. This system has a large time delay, a nonlinear temperature characteristics, a perturbation, a disturbance, etc. In this paper, we show that RHC can easily be applied to the system to track the desired temperature, since it takes the receding horizon strategy for both controller and filter.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.444-447
• /
• 1995

The satellite in a circular orbit about a planet with disturbances and a three-degree-of-freedom (3DOF) structure under seismic excitations are modeled by the linear stochastic differential equations. Then the risk-sensitive optimal control method is applied to those equations. The mean and the variance of the cost function varies with respect to the risk-sensitivity parameter, .gamma.$_{RS}$ . For a particular risk-sensitivity parameter value, risk-sensitive control reduces to LQG control. Furthermore, the derivation of the mean square value of the state and control action are given for a finite-horizon full-state-feedback risk-sensitive control system. The risk-sensitive controller outperforms a classical LQG controller in the mean square sense of the state and the control action.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.584-586
• /
• 1997

The orthogonal polynomials have been widely employed to solve control problems, but the LQG(linear quadratic gaussian) problem remains unsolved. In this paper, we obtained the solutions of Riccati equation and covariance matrix Riccati equation by two point boundary problem and matrix fraction method using BPF(Block Pulse Function), respectively. This solutions are solved the problem of the LQG controller design.