• Journal of Power Electronics
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• v.15 no.5
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• pp.1168-1177
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• 2015

This paper presents a scheme to improve the line current distortion of power factor corrector (PFC) topology at the zero crossing point using a predictive control algorithm in both the continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The line current in single-phase PFC topology is distorted at the zero crossing point of the input AC voltage because of the characteristic of the general proportional integral (PI) current controller. This distortion degrades the line current quality, such as the total harmonic distortion (THD) and the power factor (PF). Given the optimal duty cycle calculated by estimating the next state current in both the CCM and DCM, the proposed predictive control algorithm has a fast dynamic response and accuracy unlike the conventional PI current control method. These advantages of the proposed algorithm lower the line current distortion of PFC topology. The proposed method is verified through PSIM simulations and experimental results with 1.5 kW bridgeless PFC (BLPFC) topology.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.6
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• pp.923-934
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• 1987

A flexible one-link robotic manipulator is modelled as a rotating cantilever beam with a hub and tip mass. An active control law is developed with consideration of the distributed flexibility of the arm. Equation of motion is derived by Hamilton's principle and, for modal control, represented as state variable form using Galerkin's mode summation method. Feedback coefficients are chosen to minimize the linear quadratic performance index(PI). To reconstruct the complete state vector from the measurements, an observer is proposed. In order to suppress vibration of the manipulator arm to desirable extent and to obtain accuracy of the positioning, weighting factor of input in PI is adjusted. Spillover effect due to the controller which controls several important modes is examined. Experiment is also performed to validate the theoretical analysis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.3
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• pp.45-53
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• 2015

A I-V and P-V characteristic of solar cell is changed to nonlinear by radiation and temperature. Therefore, to use efficiently PV system, operating point of PV system is must operate at maximum power point always. A performance of conventional the PO and the IC method is depend on the step size. So it has weakness which is must select optimal step size. Also, MPPT control applying PI and fuzzy control is not expected satisfactory performance, because of PI controller has fixed gain and fuzzy control has cumulative error by an integral calculus. Therefore, this paper proposes the VS-PO(Variable Stepsize - Perturbation & Observation) MPPT control that is automatically adjusted the step size according to the operating conditions. The VS-PO MPPT method proposed in this paper analyzes control characteristic about condition of radiation and compares with conventional methods. The validity of this paper proves using this results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.16-28
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• 2010

This paper proposes maximum torque control of SynRM drive using artificial intelligent(AI)PI and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal axis current for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AIPI and ANN controller and the operating characteristics controlled by maximum torque control are examined in detail.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.29-31
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on fuzzy-neural networks (FN)-PI controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses In variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.148.3-148
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• 2001

This paper deals with a problem of decreasing the airspeed and the altitude of a transport aircraft using thrust only. Such a situation can occur, if the aircraft loses all hydraulic power that drives the control surfaces. A controller for flight path angle control is designed using the model following servo control method, which is a PI-type optimal regulator. For computer simulation, a simulation model that covers a range of flight envelope is made using given linear models and trim points at some flight conditions. Nondimensional aerodynamic coefficients, derivatives and trim points that are not at the given trim points are computed by linear interpolation. The model is effective in simulation where the trim point varies. Simulation using ...

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.28 no.1
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• pp.39-44
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• 1992

Analog PID controllers have been designed to make good use of position control in industries. Recently, the importance of digital position control is emphasized for the requirements of controller which are not only to control the objects but to include various aspects such as easiness of design and implementation, simple exchange of control program and convenient communications of data between various controllers and a host computer. This study proposes a combined control method which is mixed the vaiable structure control (VSC) with the PI control for minimum time position control of DC servo motor by microcomputer. The results of test by this method show offset-free and minimum time optimal position control which is not affected by the disturbance and the system parameter variations. The validity of the proposed method comparing with the conventional PID control is proved by the response experiments.

• The KSFM Journal of Fluid Machinery
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• v.16 no.6
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• pp.5-11
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• 2013

Torque control methods of wind turbine are mainly classified into two methods: torque-mode and speed-mode methods. The traditional torque-mode method, in which generator torque proportional to square of generator speed is determined, has been chosen in many wind turbines but its response is slower as they are larger in multi-MW size. Torque control methods based on both speed-mode and torque-mode can be used to make response of wind turbine faster. In this paper, two torque control methods based on the traditional torque-mode method are applied to a 2.75 MW wind turbine. It is shown through some simulation results for real turbulence wind speeds that torque control method based on torque-mode has the merit of reducing fluctuations of generated power than PI controller based on speed-mode.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.48-55
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• 2004

This study presents a genetic algorithm-based method fur optimizing control parameters in the pressure control of electro-hydraulic pump with variable displacement. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics and search the optimal control parameters maximizing a measure that evaluates the performance of a system. Four control gains of the PI-PD cascade controller for an electro-hydraulic pressure control system are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that genetic algorithm is an efficient scheme in optimizing control parameters of the pressure control of electro-hydraulic pump with variable displacement.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.