• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제3권1호
• /
• pp.1-6
• /
• 2003

The Delayed Feedback Control method (DFC) proposed by Pyragas applies an input based on the difference between the current state of the system, which is generating chaos orbits, and the $\tau$-time delayed state, and stabilizes the chaos orbit into a target. In DFC, the information about a position in the state space is unnecessary if the period of the unstable periodic orbit to stabilize is known. There exists the fault that DFC cannot stabilize the unstable periodic orbit when a linearlized system around the periodic point has an odd number property. There is the chaos control method using the prediction of the $\tau$-time future state (PDFC) proposed by Ushio et al. as the method to compensate this fault. Then, we propose a method such as improving the fault of the DFC. Namely, we combine DFC and PDFC with parameter W, which indicates the balance of both methods, not to lose each advantage. Therefore, we stabilize the state into the $\tau$ periodic orbit, and ask for the ranges of Wand gain K using Jury' method, and determine the quasi-optimum pair of (W, K) using a genetic algorithm. Finally, we apply the proposed method to a discrete-time chaotic system, and show the efficiency through some examples of numerical experiments.

• 대한안전경영과학회:학술대회논문집
• /
• 대한안전경영과학회 2000년도 추계학술발표논문집
• /
• pp.203-216
• /
• 2000

In this paper, we Investigate rationales for feedback adjustment using some techniques associated with automatic process control based on some nonstatioary disturbance models. Feedback-control schemes are often operated so that the nature of the disturbance that is being compensated is concealed and unusual deviations from the target cannot be taken account of. In this connection feedback control schemes is useful to extend the idea of common causes and special causes to such systems. Minimum-cost feedback schemes are discussed for some simple ,but practically interesting ,models.

• 대한전자공학회논문지
• /
• 제26권8호
• /
• pp.1161-1166
• /
• 1989

In this paper, a hierarchical state feedback control method is proposed for the optimal tracking of large-scale discrete-time systems with time-delays. The state feedback gain matrix and the compensation vector are computed from the optimal trajectories of the state variables and control inputs obtained hierarchically by the open-loop control method based on the interaction prediction method. The resulting feedback gain matrix and the compensation vector are optimal for the given initial condition. Computer simulation results show that the proposed method has better control performance and fewer second level iterations than the Tamura method.

• Journal of Electrical Engineering and Technology
• /
• 제8권6호
• /
• pp.1389-1399
• /
• 2013

A discrete controller is designed for low power dc-dc switched mode power supplies. The approach is based on time domain and the control loop continuously and concurrently tunes the compensator parameters to meet the converter specifications. A digital state feedback control combined with the load estimator provides a complete compensation, which further improves the dynamic performance of the closed loop system. Simulation of digitally controlled Buck converter is performed with MATLAB/Simulink. Experimental results are given to demonstrate the effectiveness of the controller using LabVIEW with a data acquisition card (model DAQ Pad - 6009).

• 한국항공우주학회지
• /
• 제39권2호
• /
• pp.137-143
• /
• 2011

중력구배 인공위성의 pitch 운동이 관성 모멘트 비와 편심율에 따라 혼돈계가 될 수 있다. 혼돈계의 경우 운동의 정확한 예측을 위하여 비혼돈계로 전환하는 혼돈계 제어가 필요하다. 혼돈계 제어에는 feedback control system을 사용할 수 있다. 중력구배 인공위성의 pitch 운동의 혼돈계 제어를 위하여, 비선형 pitch 운동 방정식을 선형화를 하여 linear nonautonomous system을 구하고, 이를 근거로 pitch 운동의 혼돈계 제어와 안정화(stabilization)를 위한 제어법칙을 설계하고 원래의 비선형 혼돈계 pitch 운동에 적용하였다. 설계된 pitch 운동 제어계는 두 개의 parameter를 가지는데, 혼돈계 제어와 안정화에 만족할 만한 결과를 보여주었다.

• International Journal of Naval Architecture and Ocean Engineering
• /
• 제12권1호
• /
• pp.768-783
• /
• 2020

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship's drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feedback system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.

• 제어로봇시스템학회논문지
• /
• 제6권1호
• /
• pp.104-111
• /
• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• Journal of Advanced Marine Engineering and Technology
• /
• 제38권7호
• /
• pp.806-813
• /
• 2014

가솔린엔진에서 흡기계를 통하여 배기가스의 일부를 재연소시키는 EGR시스템은 NOx를 저감하기 위하여 양호한 배기배출특성을 나타내지만, 엔진으로 유입되는 배기가스량이 적절하지 않을 경우 불안정한 연소를 일으켜 엔진의 출력이 저하된다. 본 연구에서는 흡기관압력을 바탕으로 다양한 엔진운전조건에 따른 EGR율을 예측하는 방법을 검토하고, 이러한 예측자료를 실험적 방법을 통하여 확인하였다. 그리고 이러한 예측자료를 바탕으로 피드백 EGR제어 알고리즘을 구성한 후, 엔진운전조건에 대한 잔류 가스량을 계산한 데이터와 EGR 피드백 제어실험을 통해 얻어진 데이터를 비교한 결과를 통하여 정성적으로 유사한 결과치를 얻었다. 따라서 적용된 피드백 EGR제어 알고리즘 및 시스템은 실제 전자제어식 EGR기술에 응용될 실현 가능성을 보여주었다.

• 한국통신학회논문지
• /
• 제23권9A호
• /
• pp.2211-2220
• /
• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.

• 한국정밀공학회지
• /
• 제15권4호
• /
• pp.15-26
• /
• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.