• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.964-967
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• 1996

In this paper, we present a fuzzy-sliding controller design using genetic algorithm. We can suppress chattering and enhance the robustness of controlled system by using this controller and do that genetic algorithm can easily find out a nearly optimal fuzzy rule performance of this controller is tested by simulation of car system with two pole.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.11
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• pp.3560-3572
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• 1996

This paper presents a new discrete-time fuzzy-sliding mode controller for robust vibration control of a smart structure featuring a piezofilm actuator. A governong equation of motion for the smart beam structure is derived and discrete-time codel with mismatched uncertainties such as parameter variations is constructed ina state space. A discrete-time sliding mode control system consisting of an equivalent controller and a discontinuous controller is formulated. In the design of the equivalent part, so called an equivalent controller separation method is adopted to achieve vzster convergence to a sliding surface without extension of a sliding region, in which the system robustness maynot be guaranteed. On the other hand, the discontinuous part is constructed on the basis of both the sliding and the convergence conditions using a time-varying feedback gain. The sliding moide controller is then incorporated with a fuzzy technique to appropriately determine principal control parameters such as a discountinuous feedback gain. Experimental implementation on the forced and random vibraiton controls is undertaken in order to demonstrate superior control performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.506-506
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• 2000

To improve control performance of a non-linear system, many other researches have used the sliding mode control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However. this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network generates the control input for compensating unmodeled dynamics terms and disturbance. And, the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors to converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluating control performance of the proposed approach. tracking control simulation is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.66.5-66
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• 2001

A proposed fuzzy-sliding mode controller in this paper shows that it can reduce amount of chattering inherent to sliding mode control and it is robust against parameter uncertainties. Sliding mode control is one of the control method for nonlinear systems. It can provide good transient performance and system robustness for nonlinear system. But chattering is a serious problem of the sliding mode control. The chattering is caused by steady/state error or uncertainties of the system. There are three kinds of method that can remove chattering. First, steady-state error can be removed by adding PI controller to the system. Second, putting dead-zone in sliding surface can be insensitive uncertainties ...

• 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.

• Journal of Electrical Engineering and Technology
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• v.6 no.1
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• pp.67-75
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• 2011

The development of electric vehicle power electronics system control, composed of DC-AC inverters and DC-DC converters, attract much research interest in the modern industry. A DC-AC inverter supplies the high-power motor torques of the propulsion system and utility loads of electric vehicles, whereas a DC-DC converter supplies the conventional low-power and low-voltage loads. However, the need for high-power bidirectional DC-DC converters in future electric vehicles has led to the development of many new topologies of DC-DC converters. The nonlinear control of power converters is an active research area in the field of power electronics. This paper focuses on the use of the fuzzy sliding mode strategy as a control strategy for buck-boost DC-DC converter power supplies in electric vehicles. The proposed fuzzy controller specifies changes in control signals based on the surface and knowledge on surface changes to satisfy the sliding mode stability and attraction conditions. The performance of the proposed fuzzy sliding controller is compared to that of the classical sliding mode controller. The satisfactory simulation results show the efficiency of the proposed control law, which reduces the chattering phenomenon. Moreover, the obtained results prove the robustness of the proposed control law against variations in load resistance and input voltage in the studied converter.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1253-1256
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• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.719-727
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• 2002

We proposed a decoupled adaptive fuzzy sliding-mode control scheme for a class of fourth-order nonlinear systems. The system is decoupled into two second-order systems such that each subsystem has a separate control target expressed in terms of sliding surface. For these sliding surfaces, we define main and sub target conditions. and, we made intermediate variables which are interconnected both surface conditions from the sub target sliding surface. Then, Two sets of fuzzy rule bases are utilized to represent the equivalent control input with unknown system functions of the main target sliding surface including intermediate variables. The membership functions of the THEN-part, which is used to construct a suitable equivalent control of sliding-mode control, are changed according to the adaptive law. With such a design scheme, we not only maintain the distribution of membership functions over state space but also reduce the computing time considerably. We apply the decoupled adaptive sliding-mode control to a nonlinear Cart-Pole system and confirms the validity of the proposed approach.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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• pp.580-591
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• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• Journal of KSNVE
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• v.6 no.1
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• pp.35-44
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• 1996

This paper presents a new type of fuzzy-sliding mode controller for robust tip position control of a single-link flexible manipulator subjected to parameter variations. A sliding mode controller is formulated with an assumption that imposed parameter variations are bounded so that certain deterministic performance can be guaranted. In the design of the sliding mode controller, so called moving sliding surface is adopted to minimize the reaching phase and thus mitigate system sensitivity to the variations. The sliding mode controller is then incorporated with a fuzzy technique to reduce inherently ever-existing chattering which is impediment in position control of flexible manipulators. A set of fuzzy parameters and control rules are obtained from a relation between predetermined sliding surface and representative points in the state space. Computer simulations are undertaken in order to demonstrate superior control performance of the proposed methodology.