• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.193-197
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• 2002

This paper suggests a method to design TSK(Takagi-Sugeno-Kang) fuzzy nonlinear control system for automatic steering system which contains the nonlinear component of ship's maneuvering equation. A TSk fuzzy model can be identified using input-output data and represent a nonlinear system very well. A TSK fuzzy controller can be designed systematically from a TSK fuzzy model because the consequent part of TSK fuzzy rule is a linear input-output equation having a constant term. Therefore, this paper suggests the method identifying the TSK fuzzy model and designing the TSK fuzzy controller based on the TSK fuzzy model for ship steering.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.53-67
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• 1998

This paper suggests a method designing the TSK fuzzy controller based on the TSK fuzzy model, which guarantees the stability of the closed loop system and makes the response of the closed loop system to be a desired one. This paper deals with the general type of TSK fuzzy model of which consequents are affine equations having a constant term. The TSK fuzzy controller suggested in this paper is designed by using the pole placement which developed for the linear systems and makes the closed loop system have the same behavior as a desired linear system. A reference input can be introduced to the suggested TSK fuzzy controller and an integral action also can be introduced. Simulation results reveal that the suggested methods are practically feasible. This paper deals with both the continuous systems and the discrete systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.225-231
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• 1998

TSK fuzzy system can represent effectively the behavior of a complex nonlinear system with low number of rules with the desired accuracy and guarantee the stability of the closed loop system, while the interpretation of the rules is difficult due to the functional nature of the consequents. On the contrary, fuzzy controller with singleton consequents is understandable intuitively and adjustable the rules easily due to qualitative expression of the rules. Ideally, one would like to combine the positive identification properties of TSK fuzzy system with the advantages of fuzzy controller with singleton consequents. Therefore, this paper suggests a method transforming TSK fuzzy systems into fuzzy systems with singleton consequents, and shows its application designing a fuzzy controller with singleton consequents by using the TSK fuzzy system when the behavior of a nonlinear system is described with a singleton fuzzy model by human esper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.133-136
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• 2003

This paper presents a fuzzy control scheme for backing up control of Truck-Trailer, which is nonlinear and unstable by using TSK(Takagi-Sugeno-kang) fuzzy system. The nonlinear system of Truck-Trailer was expressed by using TSK fuzzy model, and the TSK fuzzy controller was designed from TSK fuzzy model. The usefulness of the proposed algorithm for backing up truck-trailer is certificated by the computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.102-109
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• 2014

In this paper, an algorithm to design fuzzy PID controllers is proposed. The proposed controllers are composed of fuzzy rules of which consequences are linear PID controllers and are designed with help of TSK fuzzy controllers. TSK fuzzy controllers are designed from TSK fuzzy model using pole assignment and have outstanding ability making the output response of nonlinear systems similar to the desired one. However, because of its structure complexity the TSK fuzzy controller is difficult to be used in industry. The proposed controllers have PID controller structure which can be easily realized, and are designed by using the data obtained from control simulations with TSK fuzzy controllers. To verify the proposed algorithm, two example simulations are performed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.325-333
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• 2016

This paper proposes controller design algorithms for a ceramic soldering iron temperature control system, and reports their effectiveness in a control experiment. Because the responses of the ceramic soldering iron temperature to the control input are non-linear and very slow, precise modeling and controller design is difficult. In this study, the temperature characteristics of a ceramic soldering iron are represented by TSK fuzzy models consisting of TSK fuzzy rules. In the fuzzy rules, the premise variable is the control input and the consequences are the transfer functions. The transfer functions in the fuzzy model were obtained from the step input responses. As the responses of the ceramic soldering iron temperature are very slow, it is difficult to obtain the complete step input responses. This paper proposes a genetic algorithm to obtain the transfer functions from an incomplete step input responses, and showed its effectiveness in examples. This paper also reports a fuzzy controller design method from the TSK fuzzy model and examples. The proposed methods were applied to the temperature control experiments of ceramic iron. The TSK fuzzy model consisted of 7 TSK fuzzy rules, and the consequences were PI controllers. The experimental results of the proposed fuzzy PI controller were superior to the linear controller and were as good as in previous studies using a fuzzy PID controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.50-58
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• 2017

For nonlinear control system design, there are many studies based on TSK fuzzy model. However, TSK fuzzy modelling needs nonlinear dynamic equations of the object system or a data set fully distributed in input-output space. This paper proposes an modelling technique using only step input response data. The technique uses also the genetic algorithm. The object systems in this paper are nonlinear to control input variable or output variable. In the case of nonlinear to control input, response data obtained with several step input values are used. In the case of nonlinear to output, step input response data and zero input response data are used. This paper also presents a fuzzy controller design technique from TSK fuzzy model. The effectiveness of the proposed techniques is verified with numerical examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.85-90
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• 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 Institute of Electronics Engineers of Korea CI
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• v.39 no.1
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• pp.48-59
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• 2002

TSK(Takagi-Sugeno-Kang) fuzzy models with linear equations consequents, which represent complex nonlinear systems very well with a few rules, can be easily identified systematically by using input-output data. Many algorithms designing TSK fuzzy controllers based on TSK fuzzy models, which guarantees the stability of the closed system, have been suggested. On the contrary, singleton fuzzy models with singleton consequents can be easily understood and adjusted. In this paper, in order to utilize the merits of TSK fuzzy systems and singleton fuzzy systems, an algorithm transforming a TSK fuzzy model into a singleton fuzzy model having the same input-output relation is suggested. The suggested algorithm is applied to a fuzzy modelling example and a fuzzy controller design example.