• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.1-6
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• 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.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.203-216
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• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1161-1166
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• 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
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• v.8 no.6
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• pp.1389-1399
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• 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).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.2
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• pp.137-143
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• 2011

The pitch motion of a gravity-gradient satellite can be chaotic, depending on the ratio of mass moments of inertia and the eccentricity of the satellite orbit. For a precise prediction of motion, chaotic pitch motion has to be changed to non-chaotic motion. Feedback control can be used to obtain nonchaotic pitch motion. For chaos control and stabilization of the pitch motion of a gravity-gradient satellite, a feedback control system is designed, based on the linear nonautonomous system obtained by linearizing the nonlinear pitch motion. The control law obtained has two parameters and is applied to chaotic nonlinear pitch motion. The nonlinear control system satisfies the proposed control objectives in the range of the nonchaotic parameter space.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.768-783
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• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.104-111
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• 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
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• v.38 no.7
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• pp.806-813
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• 2014

The EGR system being reburned the part of the exhaust gas through intake system indicates more favorable emission characteristics to reduce NOx in a gasoline engine, but the case of inappropriate exhaust gas quantity induced from engine is fallen engine power caused by unstable combustion. In this study, we examined a method to predict EGR ratio according to various engine operation condition based by intake manifold pressure and confirmed such a prediction data through an experimental method. And after having constituted feedback EGR control algorithm in a base with such a prediction data, we acquired qualitatively similar results by having compared data provided through an EGR feedback control experiment with the data which calculated quantity of residual gas for the engine operation condition. Therefore, the applied algorithm and the system for feedback EGR control showed feasibility applied to real electronic control EGR technology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2211-2220
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• 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.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.15-26
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• 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.