• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.933-935
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• 1999

In this paper, we present model predictive control scheme based on neural network to control discrete-time chaotic systems. We use a feedforward neural network as nonlinear prediction model. The training algorithm used is an adaptive backpropagation algorithm that tunes the connection weights. And control signal is obtained by using gradient descent (GD), some kind of LMS method. We identify that the system identification results through model prediction control have a great effect on control performance. Finally, simulation results show that the proposed control algorithm performs much better than the conventional controller.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.235-244
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• 2014

This article introduces recent trends in RHC (Receding Horizon Control), also known as MPC (Model Predictive Control), that has been well recognized in industry and academy as a systematic approach for optimal design and constraint management. Constrained and robust RHCs will be briefly reviewed with milestone results. Among the diverse developments and achievements of RHCs, implementation issues will be focused on, together with the latest applications. In particular, this article introduces results on how to solve a finite horizon open-loop optimal control problem in an efficient way, together with code generation for real-time execution and easy implementation. Instead of traditional applications such as refineries and petrochemical plants, this article highlights some selected emerging applications, such as energy management systems and mechatronics, that have resulted from state-of-the-art high performance computing power and advanced numerical schemes.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.468-473
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• 1998

A mathematical model was developed for a continuous re-actor in which free radical polymerization of methyl methacrylate (MMA) occurred. Elementary reactions considered in this study were initiation, propagation, termination, and chain transfers to monomer and solvent. The reactor model took into account the density change of the reactor contents and the gel effect. A control system was designed for a continuous reactor using extended Kalman filter (EKF) based non-linear model predictive controller (NLMPC) to control the conversion and the weight average molecular weight of the polymer product. Control input variables were the jacket inlet temperature and the feed flow rate. For the purpose of validation of the control strategy, on-line digital control experiments were conducted with densitometer and viscometer for the measurement of the polymer properties. Despite the com-plex and nonlinear features of the polymerization reaction system, the EKF based NLMPC performed quite satisfactorily for the property control of the continuous polymerization reactor.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.11-16
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• 2014

In this paper, the two-wheels robot using a predictive controller to maintain the balance of the posture control in real time have been examined. A reaction wheel pendulum control method is adopted to maintain the balance while the bicycle robot is driving. The objective of this research was to design and implement a self-balancing algorithm using the dsPIC30F4013 embedded processor. To calculate the attitude in ARS using 2 axis gyro(roll, pitch) and 3 axis accelerometers (x, y, z). In this study, the disturbance of the posture for the asymmetrical propose to overcome the predictive controller which was a problem in the control of a remote system by introducing the two wheels of the robot controller and the linear prediction of the system controller combines the simulation was performed. Also, the robust characteristic for realizing the goal of designing a loop filter too robust controller is designed so that satisfactory stability of the control system to improve stability of the system to minimize degradation of performance was confirmed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.46-56
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• 1999

A predictive controller is designed based upon stochastic methods for compensation time-delay effect on a system which has inherent time-delay caused by the spatial separation between controllers and actuators. The predictive controller estimates current outputs through linear prediction methods and probability functions utilizing previous outputs, and minimizes the malicious phenomena caused by the time-delay in precision control systems. To demonstrate effectiveness of this control methodology, we applied this algorithm for the control of a tele-operated DC servomotor. The experimental results show that this predictive controller is superior to the PID controller in terms of convergence-characteristics, and show that this controller expands the maximum allowable time-delay for a system maintaining the stability. Since the proposed predictor does not require models of plants - it requires only stochastic information for outputs --, it is a general scheme which can be applied for the control of systems which are difficult to model or the compensator of PID control.

• Journal of Community Nutrition
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• v.6 no.3
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• pp.121-124
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• 2004

Objective : The paper aims to provide predictive coefficients via BIA for the assessment of body composition in children and adolescents to serve clinical as well as research purposes. Methods : Body composition via dual-energy x­ray absorptiometry (DXA) and bioelectric impedance as well as other anthropometric index were derived from meaurements on 1026 children and adolescents aged from 6 to 18 years from Beijing City. The best subset regression and principle component analysis were adopted to build the predictive coefficients with the logarithm of body composition via DXA as response variable. Results : Condition index ${\varphi}$ of fat-free mass multiple linear regression achieves 113.49 and 91.18 for males and females respectively, demonstrating severe multicollinearity among anthropometric indexes in children and adolescents. BIA predictive coefficients base on the best subset regression and principle component analysis boast a content predictive value for lean mass ($r^2$ = 0.9697 and 0.9664 for boys and girls respectively, p < 0.0001) and for Fat$\%$ ($r^2$ = 0.7705 and 0.6959 for boys and girls respectively, p < 0.0001). Conclusions : BIA method is applicable for the prediction of body composition for children and adolescents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4B
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• pp.338-344
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• 2002

This paper proposes a transient (predictive) connection admission control (CAC) scheme using the transient quality of service (QoS) measure for CDMA cellular systems with bursts On-Off sources. We need an approximate and bounded approach for real-time CAC applications. We derive the transient outage probability as the QoS measure using the Chernoff bound and the refuted large deviation approximation. Numerical results show that the predictive CAC is a promising approach for the multicell CDMA systems.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 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.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.252-257
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• 2005

In this work, we propose a simple but efficient method to design a target temperature trajectory for pulling speed tracking control of the crystal grower in the Czochralski crystallization process. In the suggested method, the model predictive control strategy is used to incorporate the complex dynamic effect of the heater temperature on the pulling speed into the temperature trajectory design quantitatively. The feedforward trajectories designed by the proposed method were implemented on 200 mm and 300 mm silicon crystal growers in the commercial Czochralski process. The application results have demonstrated its excellent and consistent tracking performance of pulling speed along whole bulk crystal growth.