• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.1
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• pp.116-121
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• 2018

In this paper, we propose the SIIM fuzzy Quasi-sliding mode controller for the system of a double inverted pendulum on a cart. Since it is difficult to handle this 6th-order system, we decoupled the entire system into three $2^{nd}$ order subsystem, and we designed the SIIM fuzzy Quasi-sliding mode controller for each subsystem, which was easy and did not require the derivation of the equivalent control. The stability of the entire system is guaranteed using Lyapunov function. The validity and robustness of the proposed controller are demonstrated through the computer simulation, and the results are compared with the results of former studies.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.45-52
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• 2004

The sliding mode controller with a boundary layer implemented by simplified indirect inference method (SIIM) fuzzy logic was proposed. The components of the sliding line function are used for the inputs of the SIIM fuzzy logic. The proposed control system is simple because there is no need to derive the sigmoid function and there are only four rules. The overall stability of the proposed system and the boundness of the tracking error are proved easily using the Lyapunov theory. We apply the proposed controller to control a nonlinear time-varying system. The computer simulation showed the validity of the proposed control system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.47-52
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• 2012

This paper focuses on discrete-time sliding mode control with SIIM fuzzy adaptive switching gain. The adaptive switching gain is calculated using the simplified indirect inference fuzzy logic. Two fuzzy inputs are the normal distance from the present state trajectory to the switching function and the distance from the present state trajectory to the equilibrium state. The fuzzy output $f_{out}$(k) out f k is used to adjust the speed the adaptation law depending on the location of the state trajectory. The simulation results showed that the proposed method had no chattering in case of uncertain parameter without disturbance. Moreover the convergent rate of the switching gain was faster and more stable even in case of disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.179.5-179
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• 2001

Generally, while PD-type fuzzy controller has good performance in transient period, it has uniform steady state error of response. To improve limitations of PD-type fuzzy controller, we propose a new fuzzy controller to improve the performance of transient response and to eliminate the steady state error of response. In this paper, PD-type fuzzy controller is used a simplified indirect inference method(SIIM). When the SIIM is applied, the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. The outputs of this controller are the output calculated by PD-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted by fuzzy rule according to the system state variables. To show the usefulness of the proposed controller, it is applied to 0-type 2nd-order linear system.

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

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2898-2901
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• 1999

This paper describes the design of nonlinear fuzzy I+PD controller using simplified indirect inference method. First, the fuzzy I+PD controller is derived from the conventional continuous time linear I+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 I+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 When 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 adapting with increasing the number of 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.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2839-2842
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• 1999

This paper describes the design of fuzzy PID controller using simplified indirect inference method. First, the fuzzy PID controller is derived from the conventional continuous time linear PID 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 PID 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. When 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 adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.35-41
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• 2000

This paper describes the design of fuzzy digital PID controller using a simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous-time linear digital PID controller,. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PID 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. When 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 adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated that the proposed method provides better control performance than the one proposed by D. Misir et al.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.