• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.697-699
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• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.143-143
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• 2000

A neural net based generalized predictive control(NNGPC) is presented for a hydraulic servo position control system. The proposed scheme employs generalized predictive control, where the future output being generated from the output of artificial neural networks. The proposed NNGPC does not require an accurate mathematical model for the nonlinear hydraulic system and takes less calculation time than GPC algorithm if the teaming of neural network is done. Simulation studies have been conducted on the position control of a hydraulic motor to validate and illustrate the proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.66-75
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• 1994

In this paper, we present a new GPC algorithm for plants with known disturbances. Also, based on the above algorithm unidirectional and bidirectional predictive controllers have been proposed concerning plants with input couplings between subprocesses. These predictive control algorithms can be applied to many industrial processes as a multiloop controllers. Simulation results show that the proposed algorithm has improved performance and computing time compared to conventional GPC algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.111-112
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• 2006

A new MR cylinder with built-in valves using Magneto - Rheological fluid (MR valve) is proposed for fluid power control systems. The MR fluid is a newly developed functional fluid whose obvious viscosity is controlled by the applied magnetic field intensity. This MR cylinder, which is composed of cylinder with small clearance and piston with electromagnet, has the characteristics of simple, compact and reliable structure. This paper presents a method to control the pressure of MR cylinder by using Generalized Predictive Control (GPC) algorithm. The differential pressure is controlled by applying magnetic field intensity to MR fluid. The use of GPC controller is to generate a control sequence by minimizing a cost function in such a way that the future system output is driven close to reference over finite prediction horizons. Experimental results from real time control using GPC method compared with conventional PID control method are also shown in this paper.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.225-228
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• 1990

The major difficulty in distillation column control lies in executing the set point tracking and the disturbance rejection, because of continuous changes in model order and dead time. For that, generalized predictive control(GPC) was applied to distillation column control. Recursive least square method was used to adjust the changes of model order and dead time. Quadratic progamming(QP) was used to solve the constraint problems in control action and the rate of control action. As a result of the simulation on the dynamic simulator(SPEEDUP) and the experiment on pilot plant, the ability of the set point tracking and the disturbance rejection was acceptable to apply to the real distillation column.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.4-172
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• 2001

In this paper, the Generalized Predictive Control(GPC) method based on Fuzzy Neural Networks(FNNs) is presented for the control of chaotic nonlinear systems without precise mathematical models. In our method, FNNs is used as the predictor whose parameters are tuned by the error between the actual output of nonlinear chaotic system and that of FNNs model. The parameters of GPC controller are adjusted via the gradient descent method where the difference between the actual output and the reference signal is used as a control error. Finally, computer simulation on the representative continuous-time chaotic system(Duffing system) is presented to demonstrate the effectiveness of our chaos control method.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.203-207
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• 1992

In this paper, a new GPC(Generalized Predictive Control) algorithm which is robust to disturbances isproposed. This controller minimizes the LQ cost function when the disturbance maximizes this cost function. The solution is obtained from the min-max problem which can be solved by differential game theory and has the non-recursive form which does not use the Riccati equation. Its another solution for state space models is investigated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.326-329
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• 1995

PID control has been widely used for real control system Further, there are muchreasearches on control schemes of tuning PID gains. However, there is no results for discrete-time systems with unknown time-dealy and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown parameters and unknown tiem-delay system. A numerical simulation was presented to illuatrate the effectiveness of this method.