• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1000-1003
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• 1996

Nonlinear unified predictive control(UPC) algorithm was applied to the temperature control of a batch polymerization reactor for polymethylmethacrylate(PMMA). Before the polymerization reaction is initiated, the parameters of the process model are determined by the recursive least squares(RLS) method. During the reaction, nonlinearities due to generation of heat of reaction and variation of heat transfer coefficients are predicted through the nonlinear model developed. These nonlinearities are added to the process output from the linear process model. And then, the predicted process output is used to calculate the control output sequence. The performance of nonlinear control algorithm was verified by simulation and compared with that of the linear unified predictive control algorithm. In the experiment of a batch PMMA polymerization, nonlinear unified predictive control was implemented to regulate the temperature of the reactor, and the validity of the nonlinear model was verified through the experimental results. The performance of the nonlinear controller turned out to be superior to that of the linear controller for tracking abrupt changes in setpoint.

• ICROS
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• v.2 no.1
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• pp.59-71
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• 1996

In the last decade, the model predictive control methods have enjoyed many industrial applications with successful results. Although the general predictive control methods for nonlinear chemical processes are not yet formulated, the promising features of the model predictive control methods attract attentions of many researchers who are involved with difficult but important nonlinear process control problems. Recently, the class of bilinear model has been introduced as an useful tool for examining many nonlinear phenomena. Since their structural properties are similar to those of linear models, it is not difficult to develop a robust adaptive model predictive control method based on bilinear model. We expect that the model predictive control method based on bilinear model will expand its region in the world of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

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 cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• 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.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.15-22
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• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.90-95
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• 2003

In this paper, an Adaptive Nonlinear Constrained Model Predictive Control (ANCMPC) is presented for a pH control in a fed-batch bio-reactor. The pH model is represented with Hammerstein Model. The static nonlinear part of Hammerstein model is described with the static pH model, and the dynamic linear part of the Hammerstein model is described with the CARIMA model. The parameters of the CARIMA model is estimated on-line with the input and output measurements of the system using a recursive least squares type of identi�cation algorithm. The e�ectiveness of the proposed controller is shown through simulations.

• Nuclear Engineering and Technology
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• v.33 no.1
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• pp.102-110
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• 2001

In this work, the model predictive control method was applied to a linear model and a nonlinear model of steam generators. The parameters of a linear model for steam generators are very different according to the power levels. The model predictive controller was designed for the linear steam generator model at a fixed power level. The proposed controller at the fixed power level showed good performance for any other power levels by designed changing only the input-weighting factor. As the input-weighting factor usually increases, its relative stability does so. The steam generator has some nonlinear characteristics. Therefore, the proposed algorithm has been implemented for a nonlinear model of the nuclear steam generator to verify its real performance and also, showed good performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.383-388
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• 2011

In this paper, we consider Hammerstein-Wiener nonlinear model for solid oxide fuel cell (SOFC). A nonlinear model predictive control (MPC) is proposed to trace the constant stack terminal power by Hydrogen flow as control input. After the stability of the closed-loop system with static output feedback controller is analysed by Lyapunov method, a nonlinear model predictive control based on the Hammerstein-Wiener model is developed to control the stack terminal power of the SOFC system. Simulation results verify the effectiveness of the proposed control method based on the Hammerstein-Wiener model for SOFC system.