• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.205-209
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• 1998

A synthesis of fuzzy variable structure control is proposed to design a high-angle-of-attack flight system for a modification version of the F-18 aircraft. The knowledge of the proportional, integral, and derivative control is combined into the fuzzy control that addresses both the highly nonlinear aerodynamic characteristics of elevators and the control limit of thrust vectoring nozzles. A simple gain scheduling method with multi-layered fuzzy rules is adopted to obtain an appropriate blend of elevator and thrust vectoring commands in the wide operating range. Improving the computational efficiency, an accelerated kernel for on-line fuzzy reasoning is also proposed. The resulting control system achieves the good flying quantities during a high-angle-of- attack excursion. Thus the fuzzy logic can afford the control engineer a flexible means of deriving effective control laws in the nonlinear flight regime.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.702-708
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• 2010

This paper proposes a novel linearization method for Takagi.sugeno (TS) fuzzy model. A T-S fuzzy controller consists of linear controllers based on local linear models and the local linear controllers cannot be designed independently because of overall stability conditions which are usually conservative. To use linear control theories easily for T-S fuzzy system, the linearization of T-S fuzzy model is required. However, The linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. So, a new linearization method is proposed for the T-S fuzzy system based on the idea of T-S fuzzy state transformation. For the T-S fuzzy system linearized with uncertainties, a robust optimal controller with the robustness of sliding model control(SMC) is designed.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.403-410
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• 2005

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.4
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• pp.415-424
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• 2002

In this paper, a robust fuzzy precompensated PID controller using immune algorithm for load frequency control of 2-area power system is proposed. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic based precompensation approach for PID controller. This scheme is easily implemented by adding a fuzzy precompensator to an existing PID controller. We optimize the fuzzy precompensator with an immune algorithm for complementing the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and fuzzy rules. Simulation results show that the proposed robust load frequency controller can achieve good performance even in the presence of generation rate constraints.

• Journal of Bio-Environment Control
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• v.10 no.1
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• pp.42-49
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• 2001

This study was carried out to develop a fuzzy control technique of ventilation window for controlling a temperature in a greenhouse. To reduce the fuzzy variables, the inside air temperature shop was taken as one of fuzzy variables, because the inside air temperature variation of a greenhouse by ventilation at the same window aperture is affected by difference between inside and outside air temperature, outside wind speed and the wind direction. Therefore, the antecedent variables for fuzzy algorithm were used the control error and its slop, which was same value as the inside air temperature slop during the control period, and the conclusion variable was used the window aperture opening rate. Through the basic and applicative control experiment with the control period of 3 minutes the optimum ranges of fuzzy variables were decided. The control error and its slop were taken as 3 and 1.5 times compared with target error in steady state, and the window opening rate were taken as 30% of full size of the window aperture. To evaluate the developed fuzzy algorithm in which the optimized 19 rules of fuzzy production were used, the performances of fuzzy control and PID control were compared. The temperature control errors by the fuzzy control and PID control were lower than 1.3$^{\circ}C$ and 2.2$^{\circ}C$ respectively. The accumulated operating size of the window, the number of operating and the number of inverse operating for the fuzzy control were 0.4 times, 0.5 times and 0.3 times of those compared with the PID control. Therefore, the fuzzy control can operating the window more smooth and reduce the operating energy by 1/2 times of PID control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.454-457
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• 2004

In this paper, control method proposed for effective speed control of the induction motor indirect vector control. For the induction motor drive, indirect vector control scheme that controls torque current and flux current of the stator current independently so that it can have improved dynamics. Also, neuro-fuzzy algorithm employed for torque current control in order to optimal speed control The proposed neuro-fuzzy algorithm can be applied to the precise speed control of an induction motor drive system or the field of any other power systems.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.222-226
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• 2007

In this paper we composed the dual fuzzy rules using each region of specific points and $e-{\Delta}e$ phase plane In order to make dual fuzzy rule base. We composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. proposed method is alternately use at specific points of $e-{\Delta}e$ phase plane with two fuzzy control rules that is one control rule occruing the steady state error in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Also, two fuzzy control rules in the $e-{\Delta}e$ phase plane decide the change time according to response characteristics of plants. In order to confirm thef proposed algorithm. As the results of experiments through the hydraulic servo motor control system with a DSP processor, We verified that proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.61-67
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• 2008

In this paper proposed the solar tracking system to use a fuzzy based on PC in order to increase an output of the PV array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up. nonlinear altitude and azimuth. In this paper designed a fuzzy controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.160-166
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• 2005

In this paper, to overcome drawbacks of FLC a self tuning fuzzy sliding mode controller is proposed, which controls the position of excavator's attachment, which can be regarded as an ill-defined system. It is reported that fuzzy logic theory is especially useful in the control of ill-defined system. It is important in the design of a FLC to derive control rules in which the system's dynamic characteristics are taken into account. Control rules are usually established using trial and error methods. However, in the case where the dynamic characteristics vary with operating conditions, as in the operation of excavator attachment, it is difficult to find out control rules in which all the working condition parameters are considered. Experiments are carried out on a test bed which is built around a commercial Hyundai HX-60W hydraulic excavator. The experimental results show that both alleviation of chattering and performance are achieved. Fuzzy rules are easily obtained by using the proposed method and good performance in the following the desired trajectory is achieved. In summary, the proposed controller is very effective control method for the position control of the excavator's attachment.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.