• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.605-613
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• 2001

In this paper, we design a robust adaptive controller for a nonlinear system with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered has unknown nonlinear functions being influenced by external disturbance. The upper bound of unknown nonlinear functions at each time is estimated by using a disturbance adaptation law. The estimated nonlinear functions are used to design a stabilizing function a control input. Tuning function is used to estimates unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically. The effectiveness of the proposed controller is investigated by computer simulation.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.703-705
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• 2000

In this paper, we design a robust adaptive controller for a nonlinear systems with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered in this paper has unknown nonlinear functions being influenced by external disturbance. The upper bounds of unknown nonlinear functions at each time is estimated by using disturbance adaptation law. The estimated nonlinear functions are used to design stabilizing function and control of input. Tuning function is used to estimate unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically fast.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.37.2-37
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• 2001

This paper addresses the robust adaptive output feedback tracking for nonlinear systems under disturbances whose bounds are unknown. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The State estimation is solved using K-filters, together with the construction of a bound of an error in the state estimation due to the perturbation of the disturbance. Tuning functions are used to estimate unknown system parameters without overparametrization. The proposed control algorithm ensures that the out put tracking error converges to a residual set which can be arbitrarily small, while maintaining the boundedness of all other variables. A simulation shows the effectiveness of the proposed approach

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1759-1760
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• 2008

This paper proposes a neural network(NN)-based adaptive sliding mode controller for discrete-time nonlinear systems. By using disturbance estimation technique, a sliding mode controller is designed, which forces the sliding variable to be zero. Then, NN compensator with hidden-layer-to-output-layer weight update rule is combined with sliding mode controller in order to reduce the error of the estimates of both disturbances and nonlinear functions. The whole closed loop system rejects disturbances excellently and is proved to be ultimately uniformly bounded(UUB) provided that certain conditions for design parameters are satisfied.

• Journal of Drive and Control
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• v.9 no.3
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• pp.16-22
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• 2012

Nowadays, electro hydrostatic actuator (EHA) has shown great advantages over the conventional hydraulic actuators with valve control system. This paper presents a position control for an EHA using a modified back stepping controller. The controller is designed by combining a backstepping technique and adaptation laws via special Lyapunov functions. The control signal consists of an adaptive control signal to compensate for the nonlinearities and a simple robust structure to deal with a bounded disturbance. Experiments are carried out to investigate the effectiveness of the proposed controller.

• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.9-14
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• 2013

The criterion of zero-error probability provides a limitation on error probability functions being used for adaptive systems when the error samples are shifted by the influence of DC-bias noise. In this paper, we employ a bias term in the error distribution and propose a new criterion of the biased zero-error probability with error being zero. Also, by maximizing the proposed criterion on expanded filter structures, a supervised adaptive algorithm has been derived. From the simulation results of supervised equalization, the algorithm based on the proposed criterion yielded zero-centered and highly concentrated error samples without disturbance in the environments of strong impulsive and DC-bias noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.747-752
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• 2006

In this paper, the adaptive fuzzy sliding mode controller with two systems is designed. The existing sliding mode controller used to $approximation{\^{u}}(t)$ with discrete sgn function and sat function for keeping the state trajectories on the sliding surface[1]. The proposed controller decrease the disturbance for uncertain control gain and This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems ate used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties ate demonstrated. Futhermore, fuzzy tuning improve tracking abilities by changing some sliding conditions. In the traditional sliding mode control, ${\eta}$ is a positive constant. The increase of ${\eta}$ has led to a significant decrease in the rise time. However, this has resulted in higher overshoot. Therefore the proposed ${\eta}$ tuning AFSMC improve the performances, so that the controller can track the trajectories faster and more exactly than ordinary controller. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.699-702
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• 1991

A servo system requires faster and more accurate dynamic responses. Generally a PD control is mainly used to obtain the precision, and in the other hand a fuzzy control to improve the transient response and to cope with the nonlinearity of systems. Recently hybrid control, which is attempted to combine the advantages of PD control and a Fuzzy control was proposed, but this technique requires complicate design procedures. Therefore in this paper, a Fuzzy controller with a series of membership functions, and various sampling periods and rules, was designed on the basis of Lyapunov stability theory and auto tuning methods of input scale factors. And also it was showed to have the excellent adaptive performances against internal-external disturbances and the usefulness of this controller from the results of simulations.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.