• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.76-82
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• 2004

A robust fuzzy controller is designed to stabilize a class of solvable nonlinear descriptor systems with time-varying delay. First, a new modeling and control method for nonlinear descriptor systems is presented with a fuzzy descriptor model. A sufficient condition for the existence of the fuzzy controller is given in terms of a series of LMIs. Then, a less conservative fuzzy controller design approach is obtained based on the fuzzy rules and weights. This method includes the interactions of the different subsystems into one matrix. The effectiveness of the presented approach and the design procedure of the fuzzy controller are illustrated by way of an example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1121-1124
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• 1993

We propose two-degree-of-freedom fuzzy neural network control systems. It has a hierarchical structure of two sets of control knowledge, thus it is easy to extract and refine fuzzy rules before and after the operation has started, and the number of fuzzy rules is reduced. In addition an example application of automatic vehicle operation is reported and its usefulness is shown simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.200-208
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• 2014

This paper presents the control of an inverted pendulum system using intelligent algorithms, such as fuzzy logic and neural networks, for advanced control education. The swing up balancing control of the inverted pendulum system was performed using fuzzy logic. Because the switching time from swing to standing motion is important for successful balancing, the fuzzy control method was employed to regulate the energy associated with the angular velocity required for the pendulum to be in an upright position. When the inverted pendulum arrived within a range of angles found experimentally, the control was switched from fuzzy to proportional-integral-derivative control to balance the inverted pendulum. When the pendulum was balancing, a joystick was used to command the desired position for the pendulum to follow. Experimental results demonstrated the performance of the two intelligent control methods.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.349-354
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• 2007

This paper considers the problem of designing static output feedback controllers for nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy models. Based on linear matrix inequality technique, a new method is developed for designing fuzzy stabilizing controllers via static output feedback. Furthermore, the result is also extended to $H_{\infty}$ control. Examples are given to illustrate the effectiveness of the proposed methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.423-431
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• 2000

In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1262-1266
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• 2003

Fuzzy sliding mode controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.154-160
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• 2009

A control system composed of two fuzzy controllers is proposed to balance the pole as well as to move the cart to the center of the track of the cartpole system. The two fuzzy controllers are designed with 2 input variables respectively and their control characters are studied in order to devise a control scheme that alternates the two fuzzy controllers. It is found that the control system using the scheme works well even though there is some residual oscillations of the pole and the cart.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1264-1267
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• 2005

Fuzzy logic control has been widely applied for handling the system which has uncertainty or high robust system. Since the dynamic behaviors of the systems contain complexity and uncertainty in its parameters, several fuzzy logic controllers have been implemented to control room temperature in the field of air conditioning system. In this paper, the fuzzy logic control has been developed to control both in door temperature and humidity in the air conditioning systems. The manipulating variables are speed of compressor, heater and supply air flow rate. The microcomputer was used to interface with in system. The experimental results show the superior of multivaiable fuzzy logic control to keep room temperature and humidity in air conditioning system for the best comfortable.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.473-475
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• 1992

This paper compares performance of neural and fuzzy truck backer-upper control systems. Conventional controllers require a mathematical model of how outputs depend on inputs. Neural and fuzzy control systems offer a key advantage over conventional control systems. They are model-free controllers. Neural networks learn a control process by examples (training samples). Fuzzy systems directly encode designer's experience as IF-THEN rules. For robustness test, we gradually removed training samples for the neural controller, and fuzzy rules for the fuzzy system. The errors increased laster in the neural controller than in the fuzzy system.