• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.404-406
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• 1998

In this paper, we propose a scaling factor tuning method to improve the performance of fuzzy controller. Tuning rules and reasoning are utilized on-line to determine the scaling factors based on absolute value of the error and its difference. A adaptive evolutionary computation (AEC) is used to search for the optimal tuning rules that will maximize the fitness function. Finally, the proposed fuzzy controller is applied to the angular stabilization of an inverted pendulum.

• Advances in Computational Design
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• v.5 no.3
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• pp.233-260
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• 2020

The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.182-184
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• 2004

This paper presents a PI controller tuning method for high performance permanent magnet synchronous motor (PMSM) drives under load variations using clustering fuzzy algorithm. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by clustering fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained Simulation results show the usefulness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.243-246
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• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.735-738
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• 1997

The traffic congestion caused by the exploding increase of vehicles became one of the severest social problems. Among the various approaches to solve this problem, controlling the length of traffic signals appropriately according to the individual traffic situation would be the most plausible and cost-effective method. To design a traffic signal controller which has such a property as adaptive decision-making process, we adopt fuzzy logic control method(fuzzy traffic signal controller), Moreover, using genetic algorithms we obtain an optimized fuzzy traffic signal controller (GA-fuzzy traffic signal controller). To evaluate and validate the proposed fuzzy and GA-fuzzy traffic signal controller, simulation results are presented.

• Proceedings of the KIEE Conference
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• summer
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• pp.1248-1250
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• 2004

This paper is proposed adaptive fuzzy-neural network controller(NFC) for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on NFC 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 adaptive NFC 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.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.531-533
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• 1999

The main aim of this paper is to investigate the possibility of applying fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. In addition, this study deals with the control of the lateral motion of a mobile vehicle. A adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve control of the lateral motion of the vehicle.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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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.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.94-103
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• 2003

In case of environment requiring safety such as human body and requiring flexible shape, a conventional mechanical actuator system does not satisfy requirements. Therefore, in order to solve these problems. a research of various smart material such as EAP (Electro Active Polymer), EAC (Electro Active Ceramic) and SMA (Shape Memory Alloy) is in progress. Recently, the highest preferring material among various smart material is EP (Electrostictive Polymer), because it has very fast response time, powerful force and large displacement. The previous researches have been studied properties of polymer and simple control, but present researches are studied a polymer actuator. An EP (Electostrictive Polymer) actuator has properties which change variably ils shape and environmental condition. Therefore, in order to coincide with a user's purpose, it is important not only to decide a shape of actuator and mechanical design but also to investigate a efficient controller. In this paper, we constructed the control logic with an adaptive fuzzy algorithm which depends on the physical properties of EP that has a dielectric constant depending on time. It caused for a sub-actuator to operate at the same time that a sub-actuator system operation increase with a functional improvement and control efficiency improvement in each actuator, hence it becomes very important to manage it effectively and to control the sub-system which Is operated effectively. There is a limitation on the management of Main-host system which has multiple sub-system, hence it brings out the Multi-Vehicle Control process that disperse the task efficiently. Controlling the multi-dispersion system efficiently, it needs the research of Main-host system's scheduling, data interchange between sub-actuators, data interchange between Main-host system and sub-actuator system, and data communication process. Therefore in this papers, we compared the fuzzy controller with the adaptive fuzzy controller. also, we applied the scheduling method for efficient multi-control in EP Actuator and the algorithm with interchanging data, protocol design.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.773-775
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• 1999

In this paper, A new approach to stable adaptive fuzzy control of systems is proposed. The proposed scheme does not require an accurate mathematical model yet it guarantees an asymptotic stability. Fuzzy logic system, which has the property of universal approximator is used as an adaptive element of the proposed controller. Also this Paper proposes a fuzzy system that estimates the maximum limit of the uncertain term in the system dynamics to guarantee the Lyapunov stability. Proposed adaptive fuzzy control is applied to the DC servo motor system in order to show its good performance.