• Nuclear Engineering and Technology
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• 제38권1호
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• pp.81-92
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• 2006

In this study, the core dynamics of a PWR reactor is identified online by a recursive least-squares method. Based on the identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to designing an automatic controller for the thermal power control of PWR reactors. 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, this procedure for solving the optimization problem is repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired temperature, as well as minimizing the variation of the control rod positions. In addition, the objectives are subject to the maximum and minimum control rod positions as well as the maximum control rod speed. Therefore, a genetic algorithm that is appropriate for the accomplishment of multiple objectives is utilized in order to optimize the model predictive controller. A three-dimensional nuclear reactor analysis code, MASTER that was developed by the Korea Atomic Energy Research Institute (KAERI) , is used to verify the proposed controller for a nuclear reactor. From the results of a numerical simulation that was carried out in order to verify the performance of the proposed controller with a $5\%/min$ ramp increase or decrease of a desired load and a $10\%$ step increase or decrease (which were design requirements), it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.104-106
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• 2005

In this work, a PWR reactor core dynamics is identified online by a recursive least squares method. Based on this identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to design an automatic controller for thermal power control in PWRs. 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 procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired one, and the variation of the control rod positions. Also, the objectives are subject to maximum and minimum control rod positions and maximum control rod speed. Therefore, the genetic algorithm that is appropriate to accomplish multiple objectives is used to optimize the model predictive controller. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), is used to verify the proposed controller for a nuclear reactor. From results of numerical simulation to check the performance 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, it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.

• 제어로봇시스템학회지
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• 제1권1호
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Transactions on Control, Automation and Systems Engineering
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• 제1권1호
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• pp.16-20
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• 1999

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1322-1325
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• 1997

A new feedback control law design techniqed usign of-off thrusters for the rotational maneuver of a flexible arm is discussed in this study. a two state on-off thruster actuator is taken as a primary actuation device for theis study. The on-off thruster operation is emulated in conjunction with the conventioal minimum-time trackig control law. The actuator input region is divided into two separate parts ; one is constant input and the other is time varying tegion. the new control law has potential applicatioin for the relatively low frequency structure such as large flexible space structure being currently used in various space echnology areas.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1993년도 춘계학술대회논문집; 한국과학연구소, 21 May 1993
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• pp.140-146
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• 1993

Active noise control uses the intentional superposition of acoustic waves to create a destructive interference pattern such that a reduction of the unwanted sound occurs. In active noise control system the choice of a control structure and design of the controller are the main issues of concern. In real acoustic fields there are a vast number of noise sources with time-varying nature and the characteristics of transducers and the geometric set-up of control system are subject to change. Accordingly the control system should be designed to adapt such circumstances so that required level of performance is maintained. In this paper, the adaptive control algorithm for self-tuning adaptive controller is presented for the application in active noise control system. Self-tuning is a direct integration of identification and controller design algorithm in such a manner that the two processes proceed sequentially. The least mean square algorithm was used for the identification schemes and adaptive weighted minimum variance control algorithm was applied for self-tuning controller. Computer simulation results for self-tuning feedback controller are presented. And simulation results was shown to be useful for the situation in which the periodic noise sources act on the acoustic field.

• International Journal of Aeronautical and Space Sciences
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• 제18권2호
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• pp.315-326
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• 2017

Four quadrotor UAVs are maneuvered to guide four animals into their pen within the minimum time by creating noises of predators modeled with an exponential function. The quadrotor UAVs are controlled via PID controllers, follow time optimal trajectories, and avoid collisions through altitude separations. The stability of the proposed PID controller is analyzed and verified using MATLAB/Simulink based simulations. Proposed step by step strategies would be practical solutions of actual cattle roundup problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.13-13
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• 2000

This paper considers delay-dependent guaranteed cost control for uncertain time-delay systems with norm-bounded parametric uncertainties. A new delay-dependent condition for the existence of the guaranteed cost control law is presented in terms of linear matrix inequalities (LMI). An algorithm involving convex optimization is proposed to design a controller which guarantees the suboptimal minimum of the guaranteed cost of the closed-loop system for all admissible uncertainties.

• 전기학회논문지P
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• 제51권4호
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• pp.175-185
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• 2002

In this paper, a new fully digital current control method of UPS inverter, which is based on an internal model control, is proposed. In the proposed control system, overshoots and oscillations due to the computation time-delay are compensated by explicit incorporation of the time-delay in the current control loop transfer function. The internal model controller is adopted to a second order deadbeat reference-to-output response which means that its response reaches the reference in two sampling time including computational time-delays. That is, the average current of filter capacitor is been exactly equal to the reference current with a time lag of two sampling intervals. Therefore, this method has an essentially overshoot free reference-to-output response with a minimum possible rise time. The effectiveness of the proposed control system has been verified by the simulation and experimental respectively. From the simulation and experimental results, the proposed system is achieved the robust characteristics to the calculation time delay and parameter variation as well as very fast dynamic performance, thus it can be effectively applied to the power supply for the critical load.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.