• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.528-530
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers. this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. Furthermore, stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. A simulation is included for the control of the Duffing forced-oscillation system, to show the effectiveness and feasibility of the proposed fuzzy control method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2117-2119
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• 2001

This paper proposes a novel intelligent digital redesign technique for a class of nonlinear systems represented by input-delayed Takagi-Sugeno (TS) fuzzy systems. The digitally redesigned controller can show good performance provided that the analog controller is well-designed. The developed digital redesign technique is based on the 'state-matching', so the control performance is guaranteed as well as the stability of the system. An simulation example is included to ensure the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2005-2007
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• 2001

A controller design problem for a discrete-time Takagi-Sugeno (TS) fuzzy systems is discussed. The switching-type controller is employed in this study. A switching-type fuzzy-model-based controller is constructed based on the spirit of "devide and conquer". The design condition of this controller is formulated in terms of linear matrix inequalities (LMIs), which guarantees the global stability of the controlled TS fuzzy systems. An example is included for ensuring the effecienct of the proposed control method.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2286-2288
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• 2003

In this paper, a novel and efficient discretization method of a continuous-time Takagi-Sugeno (TS) fuzzy system is proposed. Because of the highly complex and nonlinear interaction among the subsystems, it is very difficult to develop the discretized version of continuous-tine TS fuzzy system. More precisely, to obtain the suitable discretized version, it is necessary to solve two main problems: to discretize global state equations of the continuous-tine TS fuzzy system and to hold the polytopic structure after the discretization. This paper will show the solution to above two problems. Main key idea is to transforme the sampling period into the intersampling period. Finally, To show the feasibility and the validity of the proposed method, a computer simulation is provided.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1949-1951
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• 2001

This paper deals with the control of a nonlinear experimental helicopter system by using the fuzzy-model-based control approach. The fuzzy model of the experimental helicopter system is constructed from the original nonlinear dynamic equations in the form of an affine Takagi-Sugeno (TS) fuzzy system. In order to design a feasible switching-type fuzzy-model-based controller, the TS fuzzy system is converted to a set of uncertain linear systems, which is used as a basic framework to synthesize the fuzzy-model-based controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.56-58
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• 2006

Camera calibration in machine vision is the process of determining the intrinsic cameara parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.211-216
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• 2004

In this thesis, we present the Takagi-Sugeno-Kang (TSK) fuzzy model based adaptive controller and adaptive identification for a general class of uncertain nonlinear dynamic systems. We use an estimated model for the unknown plant model and use this model for designing the controller. The hybrid adaptive control combined direct and indirect adaptive control based on TSK fuzzy model is constructed. The direct adaptive law can be showed by ignoring the identification errors and fails to achieve parameter convergence. Thus, we propose an TSK fuzzy model based hybrid adaptive (HA) law combined of the tracking error and the model ins error to adjust the parameters. Using a Lyapunov synthesis approach, the proposed hybrid adaptive control is proved. The hybrid adaptive law (HA) is better than the direct adaptive (DA) method without identifying the model ins error in terms of faster and improved tracking and parameter convergence. In order to show the applicability of the proposed method, it is applied to the inverted pendulum system and the performance is verified by some simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.881-886
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent tile complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also, by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of tile digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a TS fuzzy model for the chaotic Lorentz system is used as an example to guarantee the stability and effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.526-529
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• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.711-719
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• 2009

This paper presents a design scheme of torque control for wheeled mobile robot(WMR) to asymptotically track the target reference trajectory. By considering the kinematic model of WMR, trajectory tracking control generates the desired tracking trajectory, which is transformed into the command velocity vector for the real WMR to track the target reference trajectory. The dynamic equation of the state error between the target reference trajectory and the desired tracking trajectory is represented by Takagi-Sugeno fuzzy model, and this model is used as the reference model for the real mobile robot error dynamics to follow. The control parameters are updated by adaptive laws that are designed for the error states of the real WMR to asymptotically follow the states of reference error model for the desired tracking trajectory. The proposed control is applied to a typical wheeled mobile robot and simulation studies are carried out to verify the validity and effectiveness of the control scheme.