• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.135-137
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• 1995

A new design method is proposed for a fuzzy PD controller. By analyzing phase plane characteristics we can build and optimize the rule base of fuzzy logic controller. Also, a new gain tuning method is used to improve performance in the transient and steady state. The improved performance of the new methodology is shown by an application to the design of control system with a highly nonlinear actuator.

• Smart Structures and Systems
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• v.23 no.1
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.389-394
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• 2004

This paper proposes a speed estimation method using FLC(Fuzzy Logic Controller) in order to realize the speed control of PMAM(Permanent Magnet AC Motor) with no speed sensor. This method uses FLC as a adaptive laws of MRAS(Model Reference Adaptive System) and estimates the rotor speed of PMAM with a difference between the reference model and the adjustable model. Speed control is performed by PI controller with the estimated speed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.12-18
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• 2010

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.697-700
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• 2005

An adaptive fuzzy sliding-mode controller (SMC) for uncertain or ill-defined single-input single-output (SISO) nonaffine nonlinear systems is proposed. By using the universal approximation property of the fuzzy logic system (FLS), it is tuned on-line to cancel the unknown system nonlinearity. We adopt a self-structuring FLS to guarantee global stability of the closed-loop system rather than semi=global boundedness. The control and adaptive laws are derived so that the estimated fuzzy parameters are bounded and the sliding condition is satisfied.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.360-363
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• 1993

Fuzzy Logic Control immitating human decision making process is a novel control strategy based on expert's experience and knowledge and many process designers are developing its applications. But it is difficult to obtain a set of rules from human operator. And there is a limitation on adjusting to environmental changes. In this paper, we proposed adaptive fuzzy algorithm to overcome these difficulties using weights added to the rules. To verify the validity of this control strategy, we have implemented this algorithm for a DC servo motor with PD-type fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1047-1051
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• 1993

A fuzzy controller of a manipulator with artificial rubber muscles is proposed. The fuzzy logic controller as a compensator is described to control the trajectory tracking of a -two link manipulator, where computed torque control method has already assumed to be applied. We shows that the fuzzy compensator with a simple adaptive scaling technique is effective for the robust control when there exist model uncertainties and/or untuned feedback gains. The effectiveness of the proposed control method is illustrated by some experimental results for a circular path tracking.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1293-1302
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• 1999

In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2158-2160
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• 2002

In this paper, we designed the adaptive fuzzy controller(AFLC) using neural network and tabu search. We tuned the weights of neural network changing adaptively input/output gain of fuzzy logic controller and the gain of fuzzy logic controller using tabu search. To evaluate the proposed method's effectiveness, we apply the proposed AFLC to the speed control of an actual AC servomotor system. The experimental results show that AFLC has the better control performance than PI controller in terms of settling time, rising time and overshoot.

• 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.