• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.57-61
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• 2001

A conceptional framework is proposed in which a general feedback predictive controller is taken to be a feedback interconnection of controller and GPC (General predictive Control). Numerical example are included to illustrate the procedure and to show the performance of the control system.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.908-913
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• 2014

In this paper, we propose a method by which QP (Quadratic Programming) problems can be solved in realtime so that we can implement the predictive control algorithm on a microcontroller system. Firstly, we derive a solution to QP problems by converting the original QP problems to its equivalent least squares with inequalities. Secondly, we propose a predictive control algorithm that can give good realtime computation performance by utilizing the fact that some parameters needed for solving QP problems can be computed offline. Finally, we illustrate that the proposed method can give good realtime features by running the C-code application constructed using the proposed method on a microncontroller system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1068-1071
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• 1993

An algorithm of fuzzy predictive sliding control is proposed to design a jet engine control system. Sliding control using predictive scheme is adopted to compensate the time delay of fuel injector. Fuzzy rule-base is also introduced to adjust the command input for suppressing the surge. The potential of the proposed algorithm is shown through simulations utilizing a typical engine-only model.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.329-332
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• 1997

This paper deals with the attitude control problem of nonlinear MIMO propeller setup. Multivariable GPC[Generalized Predictive Control] is adopted as the main controller, and it is implemented by TMS320C31 in the current paper. The main object of control is to move the propellers to wanted positions. System identification is performed to configure the system. Performance of the multivariable predictive controller implemented is shown via some experiments, which shows the controller meets the adequate control purpose.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.340-343
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• 1995

This paper consists of the position control of induction motor using Generalized Predictive Control. Full order flux observer is also used for the purpose of estimating rotor fluxes. By using Generalized Predictive Control algorithm, the improved position control is realized in this paper. The proposed control method has been implemented by a 32 bit floating point TMS320C31 DSP chip.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.145-149
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• 1993

This paper considers the adaptive predictive control problem of a system characterized by a multiplexed measurements and multirate sampling mechanism. Plant outputs are measured in various sampling rates through a multiplexed measurement system where a single common instrument is shared by several controllers. In general, output measurement sampling rate is assumed to be slower that input update rate. An adaptive predictive control algorithm is developed for systems with multiplexed measurements.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.419-424
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• 1992

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.