• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 A
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• pp.420-425
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• 1996

In this paper a robust speed controller for an induction motor is proposed. The speed controller consists or a fuzzy sliding adaptive controller(FSAC) and a sliding mode torque observer(SMTO). FSAC removes the problem or oscillations caused by discontinuous inputs of the sliding mode controller. The controller also provides robust characteristics against parameter and sampling time variations. Although, however, the performance of FSAC is better than PI controller and fuzzy controller in robustness, it generates the problem of slow response time. To alleviate this problem, a compensator, which performs feedforward control using torque signals produced by SMTO, is added. The simulation and hardware implementation results show that the proposed system is robust to the load disturbance, parameter variations, and measurement noises.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1834-1835
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• 2006

A robust adaptive position control algorithm is proposed for servomotor drive system with uncertainties and load disturbance. The proposed controller is comprised of a nominal controller and a robust control. The nominal controller is designed in the condition without all the external load disturbance, nonlinear friction and unpredicted uncertainties. The robust controller containing lumped uncertainty approximator using fuzzy-neural network(FNN) is designed to dispel the effect of uncertainties and load disturbance. The interconnection weight of the FNN can be online tuned in the sense of the Lyapunov stability theorem thus asymptotic stability of the proposed control system can be guaranteed. Finally, simulation results verify that the proposed control algorithm can achieve favorable tracking performance for the induction servomotor drive system.

• International Journal of Control, Automation, and Systems
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• 제4권2호
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• pp.178-186
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• 2006

We propose and analyze a robust adaptive fuzzy controller for nonlinear systems without a priori knowledge of the sign of the input gain function. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. The uncertain nonlinearities are captured by the fuzzy systems that have been proven to be universal approximators. The proposed control scheme completely overcomes the singularity problem that occurs in the indirect adaptive feedback linearizing control. Projection in the estimated parameters and switching in the control input are both not required. The stability of the closed-loop system is guaranteed in the Lyapunov viewpoint.

• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.32-42
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• 2001

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.341-345
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• 1997

A novel fuzzy basis function vector-based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the parameters of the compensator in the sense that both the asymptotic error convergence can be obtained for the colsed loop nonlinear control system.

• Smart Structures and Systems
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• 제29권4호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.199-203
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• 1997

In this paper is proposed a new direct adaptive fuzzy controller that dan ve applied for tracking control of a class of uncertain nonlinear SISO systems. It is shown that, in the presence of the perturbations such as fuzzy approximation error and external disturbance, boundedness of all the signals in the system is ensured, while under the assumption of no perturbations, the stability of the overall system in guaranteed. Also, the concept of persistent excitation in the adaptive fuzzy control systems is introduced to guarantee the convergence and the boundedness of adaptation parameter in the proposed controllers. Simulation example shows the effectiveness of the proposed method in the presence of fuzzy approximation error and external disturbance.

• 한국생산제조학회지
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• 제19권2호
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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 information and communication convergence engineering
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• 제7권2호
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• pp.125-130
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• 2009

A robust friction compensation scheme is proposed in this paper. The recurrent fuzzy neural network and friction parameter observer are developed with sliding mode based controller in order to obtain precise position tracking performance. For a servo system with incomplete identified friction parameters, a proposed control scheme provides a satisfactory result via some experiment.

• 한국지능시스템학회논문지
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• 제11권9호
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• pp.788-792
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• 2001

일반적으로 슬라이딩모드 제어는 외란이나 시스템의 변수변환에 강인한 특성을 가지나, 그 변화의 최대 경계 값을 알아야한다. 그러나, 이 값은 쉽게 얻어질 수가 없다. 퍼지 논리는 잘 정의되어 있지 않거나 복잡한 시스템의 제어기 설계에 효과적인 방법을 제시하나, 과도응답을 미리 결정할 수가 없다. 본 논문에서는 두 이론의 장점을 결합하여 새로운 제어 알고리듬을 제시한다. 최적의 퍼지변수 값을 결정하기 위하여 적응이론을 도입한다. 마지막으로 제안한 제어 알고리듬의 타당성을 살펴보기 위하여 수치적인 예를 가변길이진자시스템에 적용한다.