• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.567-572
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• 1991

This paper proposes an algorithm of fuzzy model modification which improves fuzzy relation matrix for multi-input/single output dynamic systems. Zadeh's possibility distribution plays an important role in the proposed algorithm and in the use of fuzzy models which are constructed by the proposed algorithm. The required computer capacity and time for implementing the proposed algorithm and resulting models are significantly reduced by introducing the concept of the referential fuzzy sets. A nonlinear system is given to show that the proposed algorithm can provide the fuzzy model with satisfactory accuracy.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.3
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• pp.111-119
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• 2008

This paper combines fuzzy concepts with an existing dynamic performance isolation technique in order to improve the response time performance of a Web server supporting differentiated services. A load balancing mechanism based on the fuzzy control technique is developed in such a way that ambiguous situations caused by workload estimation of cluster-based Web servers, client request rates, and dynamic request rates can be represented in an effective way. In addition, we verify that the fuzzy-based performance isolation technique improves the performance and robustness of differentiated service systems efficiently through comparing 95-percentile of response time between the fuzzy-based Performance isolation technique and the existing one, which do not use the fuzzy concept.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.7-15
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• 2005

Numerical analysis model is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses of experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.188-196
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• 2016

For the balancing control of a one-wheel mobile robot, CMG (Control Moment Gyro) can be used as a gyroscopic actuator. Balancing control has to be done in the roll angle direction by an induced gyroscopic motion. Since the dedicated CMG cannot produce the rolling motion of the body directly, the yawing motion with the help of the frictional reaction can be used. The dynamic uncertainties including the chattering of the control input, disturbances, and vibration during the flipping control of the high rotating flywheel, however, cause ill effect on the balancing performance and even lead to the instability of the system. Fuzzy compensation is introduced as an auxiliary control method to prevent the robot from the failure due to leaning aside of the flywheel. Simulation studies are conducted to see the feasibility of the proposed control method. In addition, experimental studies are conducted for the verification of the proposed control.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.510-523
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• 2022

Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of two-wheeled self-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural network active disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for online correction of active disturbance rejection controller (ADRC)'s nonlinear control rate. Firstly, the dynamic model of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensate dynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate and FNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate in real time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC and fuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect of the proposed control strategy is slightly better than ADRC and FADRC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.331-336
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• 1998

A systematic design method for PDC(Parallel Distributed Compensation)-type continuous time Takagi-Sugeno(T-S in short) fuzzy control systems which have inaccessible states is developed in this paper. Reduced-dimensional fuzzy state estimator is introduced from existing T-S fuzzy model using the PDC structure of Wang et al. [1] LMI(Linear Matrix Inequalities) problems which represent the stabililty of the reduced-dimensional fuzzy state estimator are derived. Pole placement constraints idea for each rules is adopted to determine the estimator gains and they are also revealed as LMI problems. these LMI problems are combined with Joh et al's [7][8] LMI problems for PDC -type continuous time T-S fuzzy controller design to yield a systematic design method for PDC -type continuous time T-S fuzzy control systems which have inaccessible states.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.514-518
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• 2005

In this paper an adaptive fuzzy PI controller with feedback linearizing meth od is implemented to controlling flux and torque separately in induction motor. In this paper first decoupling of torque and flux which are outputs to be controlled, is achieved by using feedback linearization methodology. Then for reducing the effect of noise and rejection of disturbance, main part of controller which is adaptive PI fuzzy controller, is designed. Coefficients of PI controller are determined by defined fuzzy rules due to error dynamic. Inputs of fuzzy system are defined sliding surfaces which consist of torque and flux errors. The main contribution of this paper is effect reduction of noise and disturbance on torque and flux which is based on fuzzy logic and nonlinear control. At last the effectiveness of the proposed control scheme in presence of noise and load disturbance is simulated and comprised to applying sliding method. The results verify better effectiveness of the proposed method for effect reduction of noise and disturbance.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.433-440
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• 2001

In this paper, we propose an automatic navigation system of ship using multivariable fuzzy control system in dynamic sea environment. The proposed multivariable fuzzy control system consists of two subsystems with three inputs and two outputs. The effectiveness of the proposed multivariable fuzzy control system is shown by simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.