• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.57-58
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• 2010

This paper proposes maximum torque control of IPMSM drive using optimal current. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using multi-MFC and ANN controller. Also, this paper proposes maximum control of IPMSM drive using approximation method. This method is decreased the burden of digital signal process(DSP) in calculation of optimal current. This paper proposes the analysis results to verify the effectiveness of the MFC and ANN controller. Also it verifies the validity of maximum torque control of IPMSM drive with optimal current.

• Communications of the Korean Mathematical Society
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• v.30 no.3
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• pp.327-337
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• 2015

This paper is concerned with the optimality conditions for optimal control problem of Belousov-Zhabotinskii reaction model. That is, we obtain the optimality conditions by showing the differentiability of the solution with respect to the control. We also show the uniqueness of the optimal control.

• 전기의세계
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• v.28 no.3
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• pp.37-50
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• 1979

The time optimal control of dynamic systems with time delay is studied with emphasis on the practical realization of controllers. An extensive survey on various methods of control is included and a result for a time optimal regulator with signle delay in control is presented and simulated on a digital computer.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.45-48
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• 1986

In this paper, time optimal positioning control of the robotic manipulator is discussed. The equations for dynamic model of the robotic manipulator are nonolinear, and each link is highly coupled. A feedback linearizing and decoupling transformation makes the dynamic model linearized and decoupled, and optimal control input for the linear and decoupled system is derived.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.112-121
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• 2001

In this paper, an engine-CVT integrated control algorithm is suggested by considering the powertrain loss, inertia torque and the CVT ratio response lag. The integrated control algorithm consists of (1) the optimal engine power calculation and (2) determining of the optimal throttle valve opening and the optimal CVT ratio. The optimal engine power is obtained by compensating the inertia torque due to the CVT ratio change and the powertrain loss that is calculated iteration procedure. In addition, an algorithm to compensate the effect of the CVT ratio response lag on the drive torque is suggested by the engine speed compensation causing the increased optimal CVT ratio. Simulation results show that the engine-CVT integrated control algorithm developed in this study makes it possible to obtain better engine operation on the optimal operating line, which results in the improved fuel economy while satisfying the driver's demand.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1455-1458
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• 1996

In this paper trajectory tracking control problems are described for a robot manipulator by using pneumatic actuator. Under the assumption that the so-called independent joint control is applied to the control system, the dynamic model for each link is identified as a linear second-order system with input time-delay by the step response. Then, an optimal servo controller is designed by taking account of such a time-delay. The effectiveness of the proposed control method is illustrated through some simulations and experiments for the robot manipulator.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.133-136
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• 2001

In this work, the problem of regulating and tracking available bit rate (ABR) traffic in ATM network. The issue of providing control signals to throttled sources at distant location from bottlenecked node is of particular interest. Network modeling and design of controller is outlined. To obtain optimal control, receding horizon control (RHC) theory is applied. Simulation results are presented in views of regulation and tracking problems with or without constraints.

• Structural Engineering and Mechanics
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• v.15 no.4
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• pp.451-462
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• 2003

In this paper, a new block iterative algorithm is presented by using the special feature of the continuous Riccati equation in the optimal shape control. Because the real-time control require that the CPU time should be as short as possible, an appropriate modal control algorithm is sought. The computing cost is less than the one of the all state feedback control. A numerical example is given to illustrate the algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.7
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• pp.743-751
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• 2013

In this paper, an approach to deal with model uncertainty using norm-optimal iterative learning control (ILC) is mentioned. Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that the suggested design method is equivalent to conventional norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.