• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.48-56
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• 2005

Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.231-238
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• 2023

For boost PFC (Power Factor Correction) converters, various control methods are being studied to achieve unity power factor and low THD (Total Harmonic Distortion) of AC input current. Among them, average current mode control, which controls the average value of the inductor current to follow the current reference, is the most widely used. However, nowadays, as advanced digital control becomes possible with the development of digital processors, predictive control of boost PFC converters is receiving attention. Predictive control is classified into predictive current mode control, which generates duty in advance using a predictive algorithm, and model predictive current control, which performs switching operations by selecting a cost function based on a model. Therefore, this paper simply explains the average current mode control, predictive current mode control, and model predictive current control of the boost PFC converter. In addition, current control under entire load and disturbance conditions is compared and analyzed through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.608-613
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• 2004

This paper is concerned with the design of a predictive PID controller, which has similar features to the model-based predictive controller. A PID type control structure is defined which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are pre-calculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with generalized predictive controller and the results are compared with generalized predictive control solutions.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.344-350
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• 1996

An adaptive predictive level control of waste heat steam boiler was studied by using mathematical models considering the inverse response. The simulation experiments of the model identification were performed by using linear and bilinear models. From the results of simulations it was found that the bilinear model represented the actual dynamic behavior of steam boiler very well. ARMA model was used in the model identification and the adaptive predictive controller. To verify the performance and effectiveness of the adaptive predictive controller used in this study the simulation results of the adaptive predictive level control for waste heat steam boiler based on bilinear model were compared to those of P, PI and PID controller. The results of simulations showed that the adaptive predictive controller provides the fast arrival to setpoint of liquid level.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.69-75
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• 2005

This paper is concerned with the design of a predictive PID controller, which has similar features to the model-based predictive controller. A PID type control structure is defined which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are pre-calculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with generalized predictive controller and the results are compared with generalized predictive control solutions.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.62.3-62
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• 2001

The predictive control is one of the nonlinear three-axis rotation methods. The desired trace of a satellite is pre-determined, and the control inputs are designed so that the satellite follows the ´predictive´ trace. The predictive control has been adapted to the research for the three-axis attitude control. In that case, the control variables are the quaternion represented the angular rates and attitude angles of the body about the three-axes. The objective of this paper is to propose to design a predictive controller for the three-axis attitude control under external disturbances. In order to do that, this paper proposes how to construct a predictive control law including disturbances and to discern them. The basic algorithm of the existent predictive control is partially modified, and the presumption and modeling of disturbances are performed ...

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.285-289
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• 1996

Fuzzy logic controllers have been shown better performance than conventional ones especially in highly nonlinear plants. These results are caused by the nonlinear fuzzy rules were not sufficient to cope with significant uncertainty of the plants and environment. Moreover, it is hard to make fuzzy rules consistent and complete. In this paper, we employed a predictive neural network to enhance the nonlinear inference capability. The predictive neural network generates predictive outputs of a controlled plant using the current and past outputs and current inputs. These predictive outputs are used in terms of fuzzy rules in fuzzy inferencing. From experiments, we found that the predictive term of fuzzy rules enhanced the inference capability of the controller. This predictive neural network can also help the controller cope with uncertainty of plants or environment by on-line learning.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.76.5-76
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• 2002

In this paper, we provide a Nash solution to predictive control problem for nonminimum phase singular nonlinear systems. Until now, there is no result on predictive control problem for this class of nonlinear systems. Chen's recent work considered predictive control problem for a class of nonlinear systems with ill-defined relative degree. Since his work is not a result considered in the feedback linearization framework, there is no a result on singular probem in his paper. In contrast to the existing predictive control result, our work considers two main obstacles (singularity and nonminimum phase) in the feedback linearization framework. For a generally formu...

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1039-1045
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• 1988

In the original incremental generalized predictive control, the receding horizon predictive control is introduced as a control law. But in this paper, we propose a generalized predictive self-tuning control using full-valued incremental controls. The control law is a mean horizon predictive control. The effectiveness of this algorithm in a variable time delay or load disturbances environment is demonstrated by computer simulation. The controlled plant is a nonminimum phase system.