• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.31 no.7
• /
• pp.18-24
• /
• 1982

We derived an optimal control of the Stochastic Bilinear Systems. For that we, firstly, formulated stochastic bilinear system and estimated its state when the system state is not directly observable. Optimal control problem of this system is reviewed on the line of three optimization techniques. An optimal control is derived using Hamilton-Jacobi-Bellman equation via dynamic programming method. It consists of combination of linear and quadratic form in the state. This negative feedback control, also, makes the system stable as far as value function is chosen to be a Lyapunov function. Several other properties of this control are discussed.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1867-1870
• /
• 1997

Waypoint guidance is a technique used to steer an autonomous vehicle along a desired trajectory. In this paper, a waypoint guidance algorithm for horizontal plane is derived by combining a line following guidance law and a turning guidance law. The line following guidance is derived based on LQR while the turning guidance is designed using rendzvous problem. Through simulation, the proposed method shows a good performance.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.6
• /
• pp.128-136
• /
• 1995

The problem addressed in this paper is that of the on-line computational burden of time-optimal control laws for quick, strongly nonlinear systems like revolute robots. It will be demonstrated that a large amount of off-line computation can be substituted for most of the on-line burden in cases of time optimization with constrained inputs if differential point-to- point specifications can be relaxed to cell-to-cell transitions. These cells result from a coarse discretization of likely swaths of state space into a set of nonuniform, contiguous volumes of relatively simple shapes. The cell boundaries approximate stream surfaces of the phase fluid and surfaces of equal transit times. Once the cells have been designed, the bang- bang schedules for the inputs are determined for all likely starting cells and terminating cells. The scheduling process is completed by treating all cells into which the trajectories might unex- pectedly stray as additional starting cells. Then an efficient-to-compute control law can be based on the resulting table of optimal strategies.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.13 no.11
• /
• pp.1072-1078
• /
• 2001

The real time optimal control algorithm of the DDC controller for chilled water and supply air temperature set-point of heating, ventilating, air-conditioning and refrigeration systems has been researched for minimization of the total power which is consumed by the chiller, chilled water pump and air handing unit fan. The study has been done by using TRNSYS program in order to analyze the central cooling system in terms of the environmental variables such as indoor cooling lead and wet-bulb temperature. This optimal control alogorithm saves more energy and is suitable for real time on-line control in comparison with conventional method.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.546-551
• /
• 2001

In this paper, an algorithm to improve the optimal engine operation is suggested by increasing the CVT shift speed. By rearranging the CVT shift dynamic equation, it is found that the CVT shift speed depends on the line pressure as well as the primary pressure. Based on the shift dynamics, an algorithm to accomplish a faster shift speed is presented by increasing the line pressure. In order to apply the algorithm, dynamic models of the line pressure control valve and the ratio control valve are obtained by considering the CVT shift dynamics and model based controllers are designed. It is found from the simulation results that fuel economy can be improved by 2% in spite of the increased hydraulic loss due to the increased line pressure.

• Proceedings of the Safety Management and Science Conference
• /
• 2004.05a
• /
• pp.61-65
• /
• 2004

As technique that can contribute in quality improvement in design process to overcome shortcoming of traditional quality control, call design or development department quality control activity that is achieved to reduce gun damage shuddering at circle minimizing change or side effect of product performance as off-line quality control. This paper discuss optimal process design of investment projects expansion and replacement investment on each line or individual in the production. Generally optimal plant design has add to a few method by Subsidiary means with use a especial method. And then in this paper, a Robust design is presented, which may be effective to the processes appraisal or improvement. We propose that should make a optimal plant design model for reducing field failure rate to assign by real data on different factors in plant system. Using this model, robust design of taguchi method used in this comprehensive method for reducing field failure rate in plant system.

• Proceedings of the KSR Conference
• /
• 2003.10c
• /
• pp.236-246
• /
• 2003

This paper is implemented a control algorithm in order to be stable and minimized to entire train traffic system at delayed case. Signal ing system is described wi th algebraic equations given for train headway, Discrete-event simulation principles are reviewed and a demonstration block signaling model using the technique is implemented. Train congestion at station entrance for short headway operation is demonstrated and the propagation of delays along a platform of trains from any imposed delay to the leading train is also shown. A rail way signaling system is by nature a distributed operation with event triggered at discrete intervals. Although the train kinematic variables of position, velocity, and acceleration are continually changing, the changes are triggered when the trains pass over section boundaries and arrive at signals and route switches. This paper deals with linear-mode1ing, stability and optimal control for the traffic on such metro line of the model is reconstructed in order to adapt the circuits. This paper propose optimal control laws wi th state feedback ensuring the stability of the modeled system for circuits. Simulation results show the benefit to be expected from an efficient traffic control. The main results are summarized as follows: 1. In this paper we develop a linear model describing the traffic for both loop lines, two state space equations have been analyzed. The first one is adapted to the situation where a complete nominal time schedule is available while second one is adapted when only the nominal time interval between trains is known, in both cases we show the unstability of the traffic when the proceeding train is delayed following properties, - They are easily implemented at law cost on existing lines. - They ensure the exponetial stability of loop system. 2. These control laws have been tested on a traffic simulation software taking into the non-linearites and the physical constraints on a metro line. By means of simulation, the efficiency of the proposed optimal control laws are shown.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.977-979
• /
• 1996

This paper presents an algorithm to control the undesirable sway of a suspended load in the crane system that has a trade-off between positioning the load and suppressing the sway of the load. The aim is to transport the load to a specified place with small sway angle as quickly as possible. Dynamic model is based on a simple pendulum driven by a velocity drive that is mostly used for actuating a trolley in industry. Proposed algorithm is composed of two parts : one is a off-line optimal trajectory generator, the other on-line tracking control. The former produces optimal trajectories minimizing energy under the speed constraint of velocity drive. The latter controls outputs to track the generated trajectories. Digital simulations and experiments are performed on a pilot crane to demonstrate the performance of the proposed control algorithm.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.9 no.1
• /
• pp.112-121
• /
• 2001

In this paper, an engine-CVT integrated control algorithm is suggested by considering the powertrain loss, inertia torque and the CVT ratio response lag. The integrated control algorithm consists of (1) the optimal engine power calculation and (2) determining of the optimal throttle valve opening and the optimal CVT ratio. The optimal engine power is obtained by compensating the inertia torque due to the CVT ratio change and the powertrain loss that is calculated iteration procedure. In addition, an algorithm to compensate the effect of the CVT ratio response lag on the drive torque is suggested by the engine speed compensation causing the increased optimal CVT ratio. Simulation results show that the engine-CVT integrated control algorithm developed in this study makes it possible to obtain better engine operation on the optimal operating line, which results in the improved fuel economy while satisfying the driver's demand.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09b
• /
• pp.95-98
• /
• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.