• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.5
• /
• pp.109-119
• /
• 1993

To improve limitations of fuzzy PI controllers especially when applied to systems of order higher than one, we propose a fuzzy PI controller that takes out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the calculation of incremental control input as in the case of conventional fuzzy PI controllers. The structure of the proposed controller was motivated by the characteristics of fuzzy PI controller that it generally gives inevitable overshoot when one tries to reduce rise time of the response especially when a system of order higher than one is under consideration. Since the undesirable characteristics of the fuzzy PI controller is caused by integrator of the controller, even though the integrator is introduced to overcome steady state error of response, we propose a controller that fuzzily clears out integrated quantities according to situation to give reduced rise time as well as small overshoots. To show the usefulness of the proposed controller, it is applied in simulations to such systems as are difficult to stabilize or difficult to get satisfactory responses by conventional fuzzy PI controllers.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1267-1271
• /
• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.429-432
• /
• 1998

This paper is to design stable fuzzy controller so as to control chaotic nonlinear systems effectively via fuzzy control system and Parallel Distributed Compensation (PDC) design. To design fuzzy control system, nonlinear systems are represented by Takagi-sugeno(TS) fuzzy models. The PDC is employed to design fuzzy controllers from the TS fuzzy models. The stability analysis and control design problems is to find a common Lyapunov function for a set of linear matrix inequalitys(LMIs). The designed fuzzy controller is applied to Rossler system. The simulation results show the effectiveness of our controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.161.4-161
• /
• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.172-175
• /
• 1996

In the paper is proposed a hierarchical self-learning fuzzy controller for balancing and position control of an circular inverted pendulum system. To stabilize the pendulum at a specified position, the hierarchical fuzzy controller consists of a supervisory controller, a self-learning fuzzy controller, and a forced disturbance generator. Simulation example shows the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.43-46
• /
• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1129-1132
• /
• 1993

This paper presents the idea of a neural fuzzy controller with application to the control of an industrial machining process. The structure of such a controller, which links the idea of a fuzzy controller and a neural network, is suggested. Results of comparative simulations indicate that the proposed neural fuzzy controller performs equally well as a fuzzy logic controller; moreover, it is more flexible and allows faster data processing.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.26 no.1
• /
• pp.76-80
• /
• 1990

DC-servomotor acts an important role in robots and manipulatirs. But the precise control of DC-motor is difficult by a using usual linear controller because of the nonlinear characteristics of DC-motor. This study suggests the use of fuzzy controller in the control of DC-servomotor speed. The fuzzy controller is designed from a fuzzy model which can represent nonlinear systems very well. Hence the fuzzy controller is very useful in the control of nonlinear systems such as DC-motor. We construct a fuzzy model of DC-servomotor, design a fuzzy controller from the fuzzy model, and compare that with a linear controller. When we use the fuzzy controller, the static ripples are reduced and the rise time is required 20% less than in using a linear controller.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.2849-2852
• /
• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.2
• /
• pp.228-233
• /
• 2004

Nowadays, the robot with various and complex functions is required. previous algorithms, however, cannot satisfy the requirement. In order to solve these problems, we introduce the 2-Layer Fuzzy Controller, which has a small number of fuzzy rules corresponding to various inputs and outputs. Also, it controls robustly and effectively an object. The main problem in the fuzzy controller is how to design the fuzzy rule. This paper designs the optimal 2-layer fuzzy controller using the Schema Co-Evolutionary Algorithm. The schema co-evolutionary algorithm can find more rapidly and excellently than simple genetic algorithm does.