• 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.

• 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...

• 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 ...

• 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.

• 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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• 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.10 no.1
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• pp.10-14
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• 2004

We present a predictive control algorithm combined with the robust robot control that is constructed on the Lyapunov min-max approach. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about the model, it is an important trend to design a robust control law that guarantees the desired properties of the manipulator under uncertain elements. In the preceding robust control work, we need to tune several control parameters in the admissible set where the desired stability can be achieved. By introducing an optimal predictive control technique in robust control we can find out much more deterministic controller for both the stability and the performance of manipulators. A new class of robust control combined with an optimal predictive control is constructed. We apply it to a simple type of 2-link robot manipulator and show that a desired performance can be achieved through the computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.155.5-155
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• 2001

The robust predictive control law which use the bound estimation is proposed for uncertain robot manipulators. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about model, it´s an important tend to design a robust control law that will guarantee the desired performance of the manipulator under uncertain elements. In the preceeding work, the robust predictive control law was proposed. In this work, we propose a class of robust predictive control of manipulators with the bound estimate technique and fe stability based on Lyapunov function is presented.

• Journal of Power System Engineering
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• v.6 no.3
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• pp.71-77
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• 2002

In this study, a dynamic model of proportional pressure control valve using the bond graph and a predictive controller are presented in the form of dynamic matrix control which is concerned with a design method of digital controller for the electro hydraulic servo system. The bond graph can be utilized for all types of systems which involve power and energy, and it is applied to a propotional pressure control valve in this study. Recently, many researchers suggested that better control performance could be obtained by means of the predictive controls with future reference input, future control output and future control error. The Predictive controller is very practical because the controller can be easily applicable to a personal computer or a microprocessor. This study investigates through numerical simulations that hydraulic system with the predictive controller shows very good control performances.

• 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.