• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.156-159
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• 1996

An adaptive predictive control for steam-reforming plant which consist of a steam-gas reformer and a waste heat steam-boiler was studied by using MIMO bilinear model. The simulation experiments of the process identification were performed by using linear and bilinear models. From the simulation results it was found that the bilinear model represented the dynamic behavior of a steam-reforming plant very well. ARMA model was used in the process identification and the adaptive predictive control. To verify the performance and effectiveness of the adaptive predictive controller proposed in this study the simulation results of steam-reforming plant control based on bilinear model were compared to those of linear model. The simulation results showed that the adaptive predictive controller based on bilinear model provides better performance than those of linear model.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.407-422
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• 2004

A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed-loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model.

• Nuclear Engineering and Technology
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• v.35 no.5
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• pp.399-411
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• 2003

A model predictive control method is applied to design an automatic controller for thermal power control in a reactor core. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the second optimal control input is not implemented and the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize the difference between the output and the desired output and the variation of the control rod position. The nonlinear PWR plant model (a nonlinear point kinetics equation with six delayed neutron groups and the lumped thermal-hydraulic balance equations) is used to verify the proposed controller of reactor power. And a controller design model used for designing the model predictive controller is obtained by applying a parameter estimation algorithm at an initial stage. From results of numerical simulation to check the controllability of the proposed controller at the $5\%/min$ ramp increase or decrease of a desired load and its $10\%$ step increase or decrease which are design requirements, the performances of this controller are proved to be excellent.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.746-750
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• 2001

During the last decades several proposals have been made in literature to use predictive control for inverter control-especially in electrical drives. These algorithms are completely different to the recursive but linear predictive algorithms known from information theory, where closed mathematical equations are used (e.g. Kalman-filters). Only few of the presented schemes have been realized in industrial applications so far. After some further progress, however, the advantage of predictive algorithms might lead to an increased number of industrial implementations in the future. Besides the common basic idea - to use the well-known but strongly non-linear behaviour of inverters to precalculate the best switching times - there are many differences in the details of these proposals. This contribution shows similarities and differences and attempts to design a 'family tree' of predictive control algorithms. This might grow to a first step to a theoretical approach to deal with predictive control schemes in a more generalised way.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.602-610
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• 2018

The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.9-15
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• 2008

In this paper, we suggest PI control with a predictive controller to progress both energy saving and coefficient of performance(COP) in a variable speed refrigeration system. The capacity and superheat are controlled simultaneously and independently by an inverter and an electronic expansion valve respectively for saving energy and improving COP in the system. The refrigeration system has long dead time in superheat inherently. The dead time makes the system difficult to achieve the satisfactory quick control response, especially superheat control response. In order to solve this problem, we designed a predictive controller based on PI control to progress superheat control performance. The control performance was investigated through some experiments to verify the effectiveness of the predictive controller.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.343-347
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• 1989

In this paper, predictive control method using actual applied input which is the weighted summation of past inputs is presented. In conventional predictive control methods, a set of control inputs is computed and in these only the first element is applied to the process at each time instant. But this predictive control method based on conventional methods considers all computed control inputs. Consequently, the characteristic of response and the reliability of the control scheme in the case of imperfact model are improved.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.206-211
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• 2004

In phase controlled rectifier, it's been known that a fast response is achieved by predictive current control without any overshoot. The frequent sampling period is essential to improve the firing accuracy in conventional predict current control. However, improving the firing accuracy if difficult to reduce the period of sampling efficiently because current sampling and predictive current control is carried out in every period and the ON-OFF current control is performed by comparing two different one. To improve the firing accuracy at the predictive current control, the calculated firing angle is loaded into the high-accuracy hardware timer. So the calculation of exact crossing point between the predictive and actual current is the most important. In this paper, the flow chart for proposed firing angle calculation algorithm is obtained for the fastest current control performance in transient state. The performance of proposed algorithm is verified through simulations and experiments.