• Transactions on Electrical and Electronic Materials
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• 제15권6호
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• pp.297-304
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• 2014

Due to the nonlinearity and complexity of the three-phase photovoltaic inverter, we propose an intelligent control based on fuzzy logic and the classical proportional-integral-derivative. The feedback linearization method is applied to cancel the nonlinearities, and transform the dynamic system into a simple and linear subsystem. The system is transformed from abc frame to dq0 synchronous frame, to simplify the state feedback linearization law, and make the close-loop dynamics in the equivalent linear model. The controls improve the dynamic response, efficiency and stability of the three-phase photovoltaic grid system, under variable temperature, solar intensity, and load. The intelligent control of the nonlinear characteristic of the photovoltaic automatically varies the coefficients $K_p$, $K_i$, and $K_d$ under variable temperature and irradiation, and eliminates the oscillation. The simulation results show the advantages of the proposed intelligent control in terms of the correctness, stability, and maintenance of its response, which from many aspects is better than that of the PID controller.

• 한국자동차공학회논문집
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• 제16권4호
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• pp.159-164
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• 2008

In diesel engine technology the drive to reduce emissions and fuel consumption with improved performance targets has led to many advances. In particular, Exhaust Gas Recirculation (EGR) and Variable Geometry Turbocharger (VGT) have played a key role in achieving these aims by permitting flexible control of the engine inlet gas charge. The full potential of these devices are difficult to achieve due to limitations in the classical control methods. However, fuzzy logic is particularly appealing due to its simple heuristic nature. The controller used in this work was designed using the Matlab Fuzzy Logic Toolbox. The overall object is to access the potential for emissions and fuel consumption reductions during transient events whilst maintaining and even improving driveability. Classical control methods (PID), as used on production engines, are examined and contrasted with an coordinated control that utilizes fuzzy logic.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.382-385
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• 2003

In this paper, in order to improve the control performance of piezoelectric actuator, an integrated control structure is proposed. The control structure consists of inverse hysteresis model , to compensate the hysteresis nonlinearty problem, and feedforward - feedback controller to give a good tracking performance. The inverse hysteresis model and neural network are used as feed-forward controller, and PID controller is used as a feedback controller. From diverse experiments it is concluded that the proposed control scheme gives good tracking performance than the classical control does.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.555-558
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• 1991

The IESF(Integral Error and State Feedback) controller, which incorporates state feedback as a modern control scheme and integral action as a classical control scheme, has better performance than that of the conventional PID controller in linear time-invariant systems. The IESF controller requires the measurement of all the state variables. But, unfortunately, it may be difficult or impossible to measure all state variables in many applications. And the IESF controller is applicable only to pole-assignable linear time-invariant system without time delay. In this paper, new IESF controller structure was proposed which performs feedback with only measurable state variables. In order to estimate the unmeasurable state variables. It was adopted the filter mode by full-order obserber. The good performance and effectiveness of the proposed controller was confirmed by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.586-589
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• 2005

Recently, the applications of unmanned system are steadily increasing. Unmanned automatic system is suitable for routine mission such as reconnaissance, environment monitoring, resource conservation and investigation. Especially, for the ocean environmental probe mission, many ocean engineers had scoped with the routine and even risky works. The unmanned surface vessel designed for sea probes can replace the periodic and routine missions such as water sampling, temperature and salinity measuring, etc. In this paper, an unmanned surface vessel was designed for ocean environmental probe missions. A classical and an adaptive fuzzy control system were designed and tested for the unmanned surface vessel. The design methodologies and performance of the surface vessel and fuzzy control algorithm were illustrated and verified with this unmanned vessel system designed for sea probes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1729-1734
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• 2005

The use of gantry crane systems for transporting payload is very common in industrial application. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. To overcome this problem, this paper describes development of an intelligent gantry crane system based on the mechatronic design. A lab-scale gantry crane is designed and then its intelligent controllers are developed. Fuzzy logic controllers are adopted, designed and implemented for controlling payload position as well as the swing angle of the gantry crane. The performance of the intelligent gantry crane system is evaluated on a hardware-in-the-loop simulation (HILS) environment. Moreover robustness of the proposed system is also evaluated. The result shows that the intelligent gantry crane system designed based on the mechatronic design approach has better performance compared with the automatic gantry crane system controlled by classical PID controllers. Moreover simulation result shows that the intelligent gantry crane system is more robust to parameter variation than the automatic gantry crane system.

• 대한전자공학회논문지
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• 제25권8호
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• pp.916-926
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• 1988

In this paper, we explore real-time implementation of various dynamic control algorithms, which use the different levels of the information of the dynamics, using a SCARA type robot to show the feasibility and effectiveness of such algorithms. For these purposes, the kinematics of the SCARA type robot is anayzed and the manipulator-actuator dynamic equations based on Lagrange mechanisms are derived. Experimental results indicate that computed torque technique and iterative learning control methods perform better than classical PID control and that these algorithms can be effectively applied to controlling industrial manipulators.

• 전기학회논문지
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• 제58권1호
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• pp.187-192
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• 2009

This paper presents a method for designing a robust controller that alleviates disturbance effects and compensates performance degradation owing to the time-delay. Disturbance observer(DOB) approach as a tool of robust control has been widely employed in industry. However, since the Pade approximation of time-delay makes the plant non-minimum phase, the classical DOB cannot be applied directly to the system with time-delay. By using a new DOB structure for non-minimum phase systems together with the Smith Predictor, we propose a new controller for reducing the both effects of disturbance and time-delay. Moreover, the closed-loop system can be made robust against uncertain time-delay with the help of a Pill controller tuning method that is based on a second-order plus dead time modeling technique.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.380-380
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• 2000

We have developed a design/analysis tool for use with Mat lab whick is named as "Parametric Robust Control and Identification(PROCI)". The tool is composed of three parts: Part i) consists of the identification of the continuous time transfer function by using either time domain input-output data or frequency response data, which might be experimentally obtained. Part ii) is the CDM synthesis of classical controller such as PID, Lead/Lag compensators. In part iii), the analysis of robustness of overall system can be dealt with. This tool allows us to analyze completely most of robustness issues with respect to the interval uncertaintyncertainty

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.