• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1648-1654
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• 2016

This paper analyzes complex vector current control for the enhanced cross-coupling compensation in accordance with parameter variation in grid-connected inverter system, and verifies through simulation and experiment. Complex vector current control is performed in the synchronous reference frame through d-q transformation. It generates cross-coupling components with rotating nominal angular frequency. In general, cross-coupling elements are compensated by decoupling terms added to output of conventional decoupling PI controller. But, it is impossible to compensate them perfectly which transient response is especially deteriorated such as large overshoot and slow tracking, when variation of grid impedance or measurement error occurs. However, complex vector current control can improve stability and response characteristic of current control regardless of the situation as before. Decoupling controller and complex vector controller are represented through complex forms, and these controllers are analyzed by using frequency response in s-domain, respectively. It is verified that complex vector controller has more superior response characteristic than decoupling controller through MATALB, PSIM and experimental in 5kW grid-connected inverter when L filter parameter is varied from 1.1mH to increase double, 2.2mH.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.4
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• pp.24-33
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• 2004

In this paper, the helicopter flight control system using online adaptive neural networks which have the universal function approximation property is considered. It is not compensation for modeling errors but approximation two functions required for feedback linearization control action from input/output of the system. To guarantee the tracking performance and the stability of the closed loop system replaced two nonlinear functions by two neural networks, weight update laws are provided by Lyapunov function and the simulation results in low speed flight mode verified the performance of the control system with the neural networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.605-614
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• 1999

This paper addresses analysis and design of a class of complex single-input single-output fuzzy control systems. In the proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Therefore, the globally stable fuzzy controller is designed without finding a common Lyapunov matrix. and shows improved perfonnance and tracking results by taking the advantages of fuzzy-model-based control theory and sliding mode control theory. Furthennore, stability analysis is conducted not Ibr the fuzzy model but for the real underlying nonlinear system. Two numerical examples are included to show the effcctiveness and feasibility of the proposed fuzzy control method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.4
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• pp.113-117
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• 2007

The study was performed on the development of system in which the bead width can be controlled. In order to control the bead width, we designed the automatic seam tracking device by attaching the probe type strain gauge sensor, motor driving slide and encoder to check the moving distance, and interface card connected MCU(80Cl96KC) upside the speed controllable carriage. Seam tracking experiments were done by changing the bead width. We compared and analyzed the sampling data which were obtained by output voltage of strain gauge sensor and rotary encoder pulse every 50ms.

• Journal of Power Electronics
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• v.16 no.5
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• pp.1629-1638
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• 2016

Differential power processing (DPP) systems are among the most effective architectures for photovoltaic (PV) power systems because they are highly efficient as a result of their distributed local maximum power point tracking ability, which allows the fractional processing of the total generated power. However, DPP systems require a high-efficiency, high step-up/down bidirectional converter with broad operating ranges and galvanic isolation. This study proposes a single, magnetic, high-efficiency, high step-up/down bidirectional DC-DC converter. The proposed converter is composed of a bidirectional flyback and a bidirectional isolated switched-capacitor cell, which are competitively cheap. The output terminals of the flyback converter and switched-capacitor cell are connected in series to obtain the voltage step-up. In the reverse power flow, the converter reciprocally operates with high efficiency across a broad operating range because it uses hard switching instead of soft switching. The proposed topology achieves a genuine on-off interleaved energy transfer at the transformer core and windings, thus providing an excellent utilization ratio. The dynamic characteristics of the converter are analyzed for the controller design. Finally, a 240 W hardware prototype is constructed to demonstrate the operation of the bidirectional converter under a current feedback control loop. To improve the efficiency of a PV system, the maximum power point tracking method is applied to the proposed converter.

• Journal of Power Electronics
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• v.18 no.3
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• pp.723-735
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• 2018

Linear proportional-integral (PI) controllers are an attractive choice for controlling the speed of induction machines because of their simplicity and ease of implementation. Fractional-order PI (FO-PI) controllers, however, perform better than PI controllers because of their nonlinear nature and the underlying iso-damping property of fractional-order operators. In this work, an FO-PI controller based on the proposed first-order plus dead-time induction motor model and integer-order (IO) controllers, such as Ziegler-Nichols PI, Cohen-Coon PI, and a PI controller tuned via trial-and-error method, is designed. Simulation and experimental investigation on an indirect field-oriented induction motor drive system proves that the proposed FO-PI controller has better speed tracking, lesser settling time, better disturbance rejection, and lower speed tracking error compared with linear IO-PI controllers. Our experimental study also validates that the FO-PI controller maximizes the torque per ampere output of the induction machine and can effectively control the motor at low speed, in field-weakening regions, and under detuned conditions.

• Journal of the Korean Solar Energy Society
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• v.35 no.2
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• pp.43-52
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• 2015

The performance of a photovoltaic array is affected by temperature, solar insolation, partial shading effect, and array configuration. Maximum power point tracking(MPPT) techniques are employed in photovoltaic systems to make full utilization of the PV array output power which depends on solar irradiation and ambient temperature. As much as MPPT is important in photovoltaic systems, many MPPT techniques have been developed. In this paper, several major existing MPPT methods are comparatively analyzed and novel hybrid MPPT algorithm is proposed. The proposed hybrid MPPT algorithm is developed in combination with traditional MPPT methods to complement each other for improving performance and mitigating partial shading effects. The proposed algorithm is implemented and validated using MATLAB/Simulink simulation tool.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1256-1258
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• 2003

The solar cells should be operated at the maximum power point because its output characteristics are greatly fluctuate on the variations of insolation, temperature and loads. To obtain maximum power from solar cell, photovoltaic system cell power system usually requires maximum power point tracking controller. This paper propose Maximum power point tracking method using zero slope of differential value of maximum power. The power compare method traces to maximum power point rapidly but oscillate on the maximum power point largely, when quantity insolation variation is big. The power compare method is traces to maximum power point slowly but oscillate maximum point on the maximum power point smally, when quantity insolation variation is small. To solve two problem of the power compare method, designed zero slope of differential value of maximum power.

• New & Renewable Energy
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• v.8 no.3
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• pp.14-22
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• 2012

Performance of photovoltaic (PV) generator systems relies on its operating conditions. Maximum power extracted from PV generators depends strongly on solar irradiation, load impedance, and ambient temperature. A most maximum power point tracking (MPPT) algorithm is based on a perturb and observe method and an incremental conductance method. It is well known the latter is better in terms of dynamics and tracking characteristics under condition of rapidly changing solar irradiation. However, in case of digital implementation, the latter has some error for determining a maximum power point. This paper presents a PID based MPPT algorithm for such PV systems. We use neural network technique for determining PID parameters by online learning approach. And we construct a boost converter to regulate the output voltage from PV generator system. Computer simulation is carried out to evaluate the proposed MPPT method and we accomplish comparative study with a perturb and observe based MPPT method to prove its superiority.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.