• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.338-341
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• 1994

This paper presents a Fuzzy-PID controller based on Fuzzy logic. Up to now PID controller has had the difficulty of obtaining the optimal gain, and Fuzzy controller has had the difficulty of determining scale factor affecting the performance of control. So that a Fuzzy-PID controller is presented here self tuning of the scale factor and optimal gain. The results of simulation show a good performance in comparison with Ziegler-Nichols controller, having the generality of determining the components of scale factor in Fuzzy rule.

• 전기학회논문지P
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• 제54권1호
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 동력기계공학회지
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• 제2권2호
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• pp.47-54
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• 1998

Underwater robotic vehicles(URVs) have been an important tool for various underwater tasks such as pipe-lining, data collection, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system is one of the most critical subsystems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. It is desirable to have an intelligent vehicle control system because the fixed-parameter linear controller such as PID may not be able to handle these changes promptly and result in poor performance. In this paper we described and analyzed a new type of fuzzy model-based controller which is designed for underwater robotic vehicles and based on Takagi-Sugeno-Kang(TSK) fuzzy model. The proposed fuzzy controller: 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule; 2) can guarantee the stability of the closed-loop fuzzy system; 3) is relatively easy to implement. Its good performance as well as its robustness to parameter changes will be shown and compared with those of the PID controller by simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2684-2686
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• 2005

This paper presents design of nonlinear controller based on genetic-fuzzy algorithm. Motor system that is included at a humanoid robot has many nonlinear parameters such as saturation, backlash and so on. So, it is hard to control a humanoid robot because of these nonlinearities. Also, tracking following ability is also reduced by these nonlinearities. In this paper, fuzzy PID controller is proposed for reducing efficiency by saturation. At that time, genetic algorithm is supplied at making fuzzy rule in order to make optimal fuzzy PID controller. Also, disturbance observer is used to reduce the efficiency of backlash. All these processes are verified by simulation and experiment in the real humanoid robot.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.465-465
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• 2000

The paper presents the fuzzy control technique to adjust the gain schedule in the fuzzy controller. The micro computer is designed to the fuzzy controller to execute the proportional gain with the data of the error and speed command. The gain schedule is the fuzzy set which execute based on the fuzzy rule. The gain schedule from the fuzzy controller is fed to the sinusoidal pulse width modulation (SPWM) inverter for control the response and speed of the induction motor. The induction motor coupling to the DC motor and tachogenerator which DC motor as a load. The test result of the fuzzy control technique in the open loop control, it provides a good response and in the closed loop control it can control speed in the any condition of load design

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.238-242
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• 2001

In this paper, we proposed an optimal identification method of the membership functions and the numbers of fuzzy rule base for the stabilization controller of the Thermal process control system by RVEGA. Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial process, they must rely on experts knowledges. So it is difficult in determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. To verify the validity of this RVEGA-based fuzzy controller, Thermal process control system, with strong nonlinear dynamics, was selected for application of this algorithm and compare with PI controller, and the empirically improved fuzzy controller.

• Structural Engineering and Mechanics
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• 제76권6호
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• pp.781-797
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• 2020

In recent years, there is an increasing interest to optimize the fuzzy logic controller with different methods. This paper focuses on the optimization of a fuzzy logic controller applied to a seismically excited nonlinear building. In most cases, this problem is formulated based on the linear behavior of the structure, however in this paper, four sets of objective functions are considered with respect to the nonlinear responses of the structure as the peak interstory drift ratio, the peak level acceleration, the ductility factor and the maximum control force. The Improved Charged System Search is used to optimize the membership functions and the rule base of the fuzzy controller. The obtained results of the optimized and the non-optimized fuzzy controllers are compared to the uncontrolled responses of the structure. Also, the performance of the utilized method is compared with various classical and advanced optimization algorithms.

• Structural Engineering and Mechanics
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• 제43권2호
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.

• 조명전기설비학회논문지
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• 제26권7호
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• pp.9-15
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• 2012

This paper is proposed the adaptive fuzzy neuro controller(AFNC) for high performance of induction motor drive. The design of this algorithm based on the AFNC that is implemented using fuzzy controller(FC) and neural network(NN). This controller uses fuzzy rule as training patterns of a NN. Also, this controller adjusts the weights between the neurons of NN to minimize the error between the command output and the actual output using the back-propagation method. The control performance of the AFNC is evaluated by analysis in various operating conditions. The results of analysis prove that the proposed control system has high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.60-64
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• 1997

Designing fuzzy controller, there are difficulties that we have to determine fuzzy rules and shapes of membership functions which are usually obtained by the amount of trial-and-error or experiences from the experts. In this paper, to overcome these defects, genetic algorithms which is probabilistic search method based on genetics and evolution theory are used to determine fuzzy rules and fuzzy membership functions. We design a series compensation fuzzy controller, then determine basic structures, input-output variables, fuzzy inference methods and defuzzification methods for fuzzy controllers. We develop genetic algorithms which may search more accurate optimal solutions. For evaluating the fuzzy controller performances through experiments upon an actual system, we design the fuzzy controllers for the speed control of a DC series motor with nonlinear characteristics and show good output responses to reference inputs.