• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.925-934
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• 2015

This paper considers the robust controller design problem for a boost DC-DC converter. Based on the Takagi-Sugeno fuzzy model-based approach, a fuzzy controller as well as a fuzzy load conductance observer are designed. Sufficient conditions for the existence of the controller and the observer are derived using Linear Matrix Inequalities (LMIs). LMI parameterizations of the gain matrices are obtained. Additionally, LMI conditions for the existence of the fuzzy controller and the fuzzy load observer guaranteeing α-stability, quadratic performance are derived. The exponential stability of the augmented fuzzy observer-controller system is shown. It is also shown that the fuzzy load observer and the fuzzy controller can be designed independently. Finally, the effectiveness of the proposed method is verified via experimental and simulation results under various conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.452-461
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• 2007

Adaptive anti-sway and trajectory tracking control of overhead crane is presented, which utilizes Fuzzy Uncertainty Observer(FUO) and Fuzzy based Variable Structure Control(FVSC). We consider an overhead crane system which can be decoupled into the actuated and unactuated subsystems with its own lumped uncertainty such as parameter uncertainties and external disturbance. First, a new method for anti-sway control using FVSC is proposed to improve the conventional method based on Lyapunov direct method, while a conventional trajectory tracking control law using feedback linearization is directly adopted. Second, FUO is designed to estimate one of the two lumped uncertainties which can compensate both of them, based on the fact that two lumped uncertainties are coupled with each other. Then, an adaptive anti-sway control is proposed by incorporating the proposed FVSC and FUO. Under the condition that the observation error is Uniformly Ultimately Bounded(UUB) within an arbitrarily shrinkable region, the overall closed-loop system is shown to be Globally Uniformly Ultimately Bounded(GUUB). In addition, the Global Asymptotic Stability(GAS) of it is shown under the vanishing disturbance assumption. Finally, the effectiveness of the proposed scheme has been confirmed by numerical simulations.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.2
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• pp.241-250
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• 2010

A recurrent adaptive model-free intelligent control with a friction estimation law is proposed to enhance the positioning performance of the mover in PMLSM system. For the PMLSM with nonlinear friction and uncertainty, an adaptive recurrent fuzzy neural network(ARFNN) and compensated control law in $H_{\infty}$ performance criterion are designed to mimic a perfect control law and compensate the approximated error between ideal controller and ARFNN. Combined with friction observer to estimate nonlinear friction parameters of the LuGre model, on-line adaptive laws of the controller and observer are derived based on the Lyapunov stability criterion. To analyze the effectiveness our control scheme, some simulations for the PMLSM with nonlinear friction and uncertainty were executed.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.89-99
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• 2008

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.170-173
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• 2005

This paper presents an Fuzzy-Neuro Observer system for an ac servo motor dirve to track periodic commands using a neuro-fuzzy observer. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.127-134
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• 2008

In this paper, we propose a robust adaptive backstepping control of induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time-varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the fuzzy neural network(FNN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.2
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• pp.163-172
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• 2010

A robust positioning control system has been studied using a friction parameter observer and a recurrent fuzzy neural network based on the sliding model. To estimate a nonlinear friction parameters of the LuGre friction model, a dual friction model-based observer is introduced. In addition, an approximating method for a system uncertainty has been developed using a recurrent fuzzy neural network technique to improve positioning performance. Experimental results have been presented to validate the performance of a proposed intelligent compensation scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.464-469
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• 2009

This paper discusses a robust observer-based output-feedback stabilization of an uncertain Takagi-Sugeno (T-S) fuzzy system in a network. In the networked control system, the input delay occurs inevitably and it is expressed by the Markovian stochastic process. To design robust sampled-data observer-based output-feedback controller, we discretize the T-S fuzzy system and represent as a jump system. Stochastic robust stabilization condition is formulated in terms of linear matrix inequalities.

• Smart Structures and Systems
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• v.29 no.4
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• pp.617-624
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• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.