• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.3
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• pp.29-39
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• 1992

We investigate a systematic design procedure of automatic rule generation of fuzzy logic based controllers for highly nonlinear dynamic systems such as an engine dynamic modle. By "automatic rule generation" we mean autonomous clustering or collection of such meaningful transitional relations from one conditional subspace to another. During the design procedure, we also consider optimaly control strategies such as minimum squared error, near minimum time, minimum energy or combined performance critiera. Fuzzy feedback control systems designed by our method have the properties of closed-loop stability, robustness under parameter variabitions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller dwsign to a highly nonlinear model of engine idling speed control.d control.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.38-50
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• 1997

This paper concerns a VHDL design and simulation of an accurate and cost-effective fuzzy logic controller (FLC). The accurcy of the proposed FLC is obtained by using the center of gravity (COG) defuzzifier that considers both membership values and spans of membership functions in calculating a crisp value. The cost-effectiveness of the proposed FLC is obtained by restructuring the conventional FLC in the following ways: Firstly, the MAX-MIN inference is inference is replaced by a read-modify-write operation that can be implemented economically in the structure of register files. Secondly, the division in the COG defuzzifier is avoided by finding the moment equilibrium point. The proposed COG defuzzifier has two disadvantages that it requires additional multipliers and it takes a lot of computation time to find the moment equilibrium point. The first disadvantage is overcome by replacing the mulitpliers with stochastic AND operations and the second disadvantage is alleviated by using a coarse-to-fine searching algorithm. The proposed FLC is described in VHDL structurally and behaviorally and whether it is working well or not is checked on SYNOPSYS VHDL simulator by using the truck backer-upper control problem.

• 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 Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.209-216
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• 2005

The controller that can control the smart base isolation system consisting of M damper and friction pendulum systems(FPS) is developed in this study. A fuzzy logic controller (FLC) is used to modulate the M damper force because the FLC has an inherent robustness and ability to handle non-linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. When earthquake excitations are applied to the structures equipped with smart base isolation system, the relative displacement at the isolation level as well as the acceleration of the structure should be regulated under appropriate level. Thus, NSGA-II(Non-dominated Sorting Genetic Algorithm) is employed in this study as a multi-objective genetic algorithm to meet more than two control objectives, simultaneously. NSGA-II is used to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can efficiently find Pareto optimal sets that can reduce both structural acceleration and base drift from numerical studies.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.1-12
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• 2004

This paper presents an adaptive fuzzy backstepping (AFB) controller for a single-link flexible joint robot in the Presence of Parametric uncertainties and external disturbances. Adaptive fuzzy logic systems are used as universal approximators to counteract the model uncertainties coming from robot dynamics and to compensate for the nonlinearities coming from adaptive backstepping method. The approach suggested herein does not require neither an additional supervisory nor a robustifying controller and guarantees that tracking error is uniformly ultimately bounded (UUB) within a sufficiently small residual set. Finally, a simulation result is given to demonstrate the robust tracking performance of proposed design method.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2458-2460
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• 2000

The goal of crane control system is transporting heavy objects to a target position as fast as possible without rope oscillation. This paper presents a GA-based fuzzy logic controller for crane system. GA is going to decide membership functions, instead of an expert. In this paper, The centers and widths of the membership function of the fuzzy sets defined over the input space, the orders and parameters of subsystems in the consequence parts are adjusted by a genetic algorithm. The effectiveness of the proposed method is verified by simulation.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.90-97
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• 2005

Magnetic flywheel system utilizes a magnetic bearing, which is able to support the shaft without mechanical contacts, and also it is able to control rotational vibration. Magnetic flywheel system is composed of position sensors, a digital controller, actuating amplifiers, an electromagnet and a flywheel. This work applies the neuro-fuzzy control algorithm to control the vibration of a magnetic flywheel system. It proposes the design skill of an optimal controller when the system has structured uncertainty and unstructured uncertainty, i.e. it has a difficulty in extracting the exact mathematical model. Inhibitory action of vibration was verified at the specified rotating speed. Unbalance response, a serious problem in rotating machinery, is improved by using a magnetic bearing with neuro-fuzzy algorithm.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.812-814
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• 1999

In this paper, we are applied the Genetic Algorithm (GA) to design of fuzzy logic controller (FLC) for a DC Servo-Motor Speed Control. GA is used to design of the membership functions and scaling factor of FLC. To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC servo-motor system with nonlinear characteristics. Experimental results show that proposed controller have better performance than those of PD controller.