• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.377-382
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• 2005

This paper presents a new method for the attitude control of planar space robots. In order to control highly constrained non-linear system such as a 3D space robot, the analytical formulation for the system with complex dynamics and effective control methodology based on the formulation, are not always obtainable. In the proposed method, correspondingly, a non-analytical but effective self-organizing modeling method for controlling a highly constrained system is proposed based on a polynomial data mining algorithm. In order to control the attitude of a planar space robot, it is well known to require inputs characterized by a special pattern in time series with a non-deterministic length. In order to correspond to this type of control paradigm, we adopt the Model Predictive Control (MPC) scheme where the length of the non-deterministic horizon is determined based on implementation cost and control performance. The optimal solution to finding the size of the input pattern is found by a solving two-stage programming problem.

• 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.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1133-1145
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• 2015

A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.

• Journal of Electrical Engineering and Technology
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• v.6 no.3
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• pp.423-430
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• 2011

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler-turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, bypass valve, and damper). The step-response matrix for the MIMO DMC is constructed using the four input and the four output variables. Online optimization is performed for the MIMO DMC using the model predictive control technique. The MIMO DMC controller is implemented in a full-scope power plant simulator with satisfactory performance.

• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.46-51
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• 1999

This paper describes the control charcteristics of thermal/flow systems. In thermal/flow systems, the transport lag plays as a dead time causing a deterioration of the controllability. Besides this, such many parameters including the temperature, pressure, and flow rate affect the system response that a control scheme which can deal with multi-input is required. Particularly in a refrigerant compressor test facility, the evaporator and condenser interact each other so that the change in the evaporator pressure cause the condenser pressure to change or vice versa. Therefore, to control the evaporator pressure, not only the cooling water flow rate in the evaporator but also the coolant flow rate in the condenser is considered. Meanwhile, the conventional PID controllers, which is suitable for a single input system, shows a large overshoot for a disturbance input. In this work, the predictive control scheme is introduced and its applicability is discussed for thermal/flow systems.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1119-1126
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• 2004

This paper presents optimized structured treatment interruption to reduce medication and establish long-term immune response against HIV-infection. Understanding HIV-related immune system control enables better HIV therapy without using full­treatments. Discrete regimen and continuous regimen characteristics are compared. Controllability of HIV-related immune system is analyzed for better understanding of optimal control in HIV therapy. Using optimal control provides more effective therapy than the full treatment without interruption in terms of controllability analysis. Case studies indicates that the proposed therapy induces long-erm non-progression while preserving high CD4 T-helper cell count and low virus load in HIV-infected patients.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1435-1437
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• 2003

In this paper, we proposed static output feedback model predictive control for Wiener models. We adopted polytopic uncertainty description of Wiener Model Predictive Control (WMPC) algorithms for considering output nonlinearities. Robust stability conditions have been presented under which the closed loop stability of static output feedback MPC is guaranteed. The proposed control law is determined from the static output feedback WMPC based on the current estimated state with explicit satisfaction of input constraints.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.19-26
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• 2005

This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.10
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• pp.506-510
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• 2002

In this paper, we study on the mutual characteristics between transmitting efficiency of pulse energy and wall plug consumed power of non-thermal Plasma for removing environmental pollutive gas of coal plant. To obtain high pulse energy of our system, we used MPC(magnetic pulse compressor) of power switch and tested their characteristics by adjusting electrode length of reactor and charging voltage in capacitor. As a result, we obtained consumed power of wall plug and a compressed pulse of voltage 110kV, rising time 500ns. Impedance of load on increasing electrode length was decreased, but electrical efficiency was increased. These results indicate we can control critical voltage of pulse corona and electrical efficiency of economic cost in power plant.

• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.43-50
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• 2013

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.