• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.58-65
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• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.60-67
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• 2002

This paper presents a control approach for designing a self tuning fuzzy controller for a synchronous generator excitation and SVC system. A combination of thyristor-controlled reactors and fixed capacitors (TCR-FC) type SVC is recognized as having the most flexible control and high speed response, which has been widely utilized in power systems, is considered and designed to improve the response of a synchronous generator, as well as controlling the system voltage. The proposed parameter self tuning algorithm of fuzzy controller is based on the steepest decent method using two direction vectors which make error between inference values of fuzzy controller and output values of the specially selected PSS reduce steepestly. Using input-output data pair obtained from PSS, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed steepest decent method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.84-89
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• 2003

Fuzzy controller is one of the succeed controller used in the process control in case of model uncertainties. But it my be difficult to fuzzy controller to articulate the accumulated knowledge to encompass all circumstance. Hence, it is essential to provide a tuning capability. There are many parameters in fuzzy controller can be adapted, scale factor tuning of normalized fuzzy controller is one of the adaptation parameter. Two adaptation methods are implemented in this work on an experimental thermal process, which simulate heating process in liquefied petroleum gases (LPG) recovery process in one of petrochemical industries: Gradient decent (GD) adaptation method; supervisory fuzzy controller. A comparison between the two methods is discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.914-922
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• 1999

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.127-130
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• 1995

Fuzzy controllers have proven to be powerful in controlling dynamic processes where mathematical models are unknown or intractable and ill-defined. The way of improving the performance of a fuzzy controller is based on making up rules, constructing membership functions, selecting a defuzzification method and adjusting input-output scaling factors. But there are many difficulties in tuning those to optimize a fuzzy controller. So, in this paper, we propose the look-up table based self-orgenizing fuzzy controller (LSOFC) which optimizes look-up values resulting from the above fuzzy processes. We use the plus-minus tuning method(PMTM), scanning the value through the processes of addition and subtraction. Simulation results demonstrate that the performance of LSOFC is far better than that of a non-tuning fuzzy controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.12
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• pp.589-594
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• 2000

The fuzzy controllers have been applied to the power system stabilizer due to its excellent properties on the nonlinear systems. But the design process of fuzzy controller requires empirical and heuristic knowledge of human experts as well as many trial-and-errors in general. This process is time consuming task. This paper presents an parameters tuning method of the fuzzy power system stabilizer using the genetic algorithm and simulated annealing(SA). The proposed method searches the local minimum point using the simulated annealing algorithm. The proposed method is applied to the one-machine infinite-bus of a power system. Through the comparative simulation with conventional stabilizer and fuzzy stabilizer tuned by genetic algorithm under various operating conditions and system parameters, the robustness of fuzzy stabilizer tuned by proposed method with respect to the nonlinear power system is verified.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.423-426
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• 2005

In this paper, we proposed a PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, $\tau_{I}$, $\tau_{D}$. by the Ziegler-Nichols formula that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and fnd the new proportion gain(Kp) and the integral time ($\tau_{I}$) from fuzzy tuner by the error and error rate of plant oueut as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant property of this algorithm is shown by simulation

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1092-1095
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• 1996

In this paper The tuning of PID controller for multi input-output is studied by using fuzzy inference. State of coupling is estimated by fuzzy inference, its results is used for tuning of PID controller to get optimum P,I,D parameter with regard to state of coupling. This method is simulated to Turbo-generating system with $2{\times}2$ multi input-output and made with electronic circuit, its response is very satisfactory.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.265-271
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• 1992

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.385-390
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• 1993

It is the purpose of this paper to present a dialogical tuning method of the sampling period in fuzzy control systems. Last year, the authors gave a dialogical tuning technique of fuzzy control system under the fixed sampling period in this symposium. In the case where sampling period is chosen larger, the response of the control system is unsatisfactory, and in the case where the sampling period is smaller, ineffective control actions are repeated. The appropriate sampling period is chosen through the step response of the closed loop fuzzy control process. As the tuning technique depends on the controlled plant, it is necessary to estimate the rough characteristics of it. The authors propose a method to decide th appropriate sampling period, by inspecting the characteristics of the plant.