• Title/Summary/Keyword: Nonlinear adaptive control

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Compensative Microstepping Based Position Control with Passive Nonlinear Adaptive Observer for Permanent Magnet Stepper Motors

  • Kim, Wonhee;Lee, Youngwoo;Shin, Donghoon;Chung, Chung Choo
    • Journal of Electrical Engineering and Technology
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    • v.12 no.5
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    • pp.1991-2000
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    • 2017
  • This paper presents a compensative microstepping based position control with passive nonlinear adaptive observer for permanent magnet stepper motor. Due to the resistance uncertainties, a position error exists in the steady-state, and a ripple of position error appears during operation. The compensative microstepping is proposed to remedy this problem. The nonlinear controller guarantees the desired currents. The passive nonlinear adaptive observer is designed to estimate the phase resistances and the velocity. The closed-loop stability is proven using input to state stability. Simulation results show that the position error in the steady-state is removed by the proposed method if the persistent excitation conditions are satisfied. Furthermore, the position ripple is reduced, and the Lissajou curve of the phase currents is a circle.

Adaptive PID Controller for Nonlinear Systems using Fuzzy Model (퍼지 모델을 이용한 비선형 시스템의 적응 PID 제어기)

  • Kim, Jong-Hua;Lee, Won-Chang;Kang, Geun-Taek
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.1
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    • pp.85-90
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    • 2003
  • This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

Direct Adaptive Fuzzy Controller for Nonaffine Nonlinear System (비어파인 비선형 시스템에 대한 직접 적응 퍼지 제어기)

  • 박장현;김성환;박영환
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.5
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    • pp.315-322
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    • 2004
  • A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

Linear/nonlinear system identification and adaptive tracking control using neural networks (신경회로망을 이용한 선형/비선형 시스템의 식별과 적응 트래킹 제어)

  • 조규상;임제택
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.5
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    • pp.1-9
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    • 1996
  • In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

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Control Method of Nonlinear System using Dynamical Neural Network (동적 신경회로망을 이용한 비선형 시스템 제어 방식)

  • 정경권;이정훈;김영렬;이용구;손동설;엄기환
    • Proceedings of the IEEK Conference
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    • 2002.06c
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    • pp.33-36
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    • 2002
  • In this paper, we propose a control method of an unknown nonlinear system using a dynamical neural network. The method proposed in this paper performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed algorithm, we simulated one-link manipulator. The simulation result showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

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Decentralized Nonlinear Voltage Control of Multi-machine Power Systems with Nonlinear Interconnections

  • Lee, Jae-Won;Yoon, Tae-Woong;Im, Sun
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.448-453
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    • 2004
  • In this paper, an adaptive robust decentralized excitation control scheme is proposed to enhance the transient stability of a multi-machine power system. We employ a state model where the terminal voltage of each generator is regarded as part of the state. Using this state model, the proposed controller is obtained in two steps: firstly, a robust controller is designed for the nominal system with no interconnection terms; then an adaptive compensator is proposed to deal with those interconnection terms, whose upper bounds are estimated. The resulting adaptive scheme guarantees the practical stability of the closed-loop, and also the uniform ultimate boundedness in the presence of disturbances.

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Autopilot design using robust nonlinear dynamic inversion method (견실한 비선형 dynamic inversion 방법을 이용한 오토파일롯 설계)

  • 김승환;송찬호
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.1492-1495
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    • 1996
  • In this paper, an approach to autopilot design based on the robust nonlinear dynamic inversion method is proposed. Both unknown parameters and uncertainty bounds are estimated and parameter estimates are used in the fast inversion. Furthermore, to get more robustness slow inversion is incorporated with MRAC(Model Reference Adaptive Control) and sliding mode control where the estimates of uncertainty bounds are used. The proposed method is applied to the pitch autopilot design of a missile system and excellent performance is shown via computer simulation.

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Hybrid Control of an Active Suspension System with Full-Car Model Using H$_{}$$\infty$/ and Nonlinear Adaptive Control Methods

  • Bui, Trong-Hieu;Suh, Jin-Ho;Kim, Sang-Bong;Nguyen, Tan-Tien
    • Journal of Mechanical Science and Technology
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    • v.16 no.12
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    • pp.1613-1626
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    • 2002
  • This paper presents hybrid control of an active suspension system with a full-car model by using H$\sub$$\infty$/ and nonlinear adaptive control methods. The full-car model has seven degrees of freedom including heaving, pitching and rolling motions. In the active suspension system, the controller shows good performance: small gains from the road disturbances to the heaving, pitching and rolling accelerations of the car body. Also the controlled system must be robust to system parameter variations. As the control method, H$\sub$$\infty$/ controller is designed so as to guarantee the robustness of a closed-loop system in the presence of uncertainties and disturbances. The system parameter variations are taken into account by multiplicative uncertainty model and the system robustness is guaranteed by small gain theorem. The active system with H$\sub$$\infty$/ controller can reduce the accelerations of the car body in the heaving, pitching and rolling directions. The nonlinearity of a hydraulic actuator is handled by nonlinear adaptive control based on the back-stepping method. The effectiveness of the controllers is verified through simulation results in both frequency and time domains.

An Adaptive Fuzzy Sliding-Mode Control for Decoupled Nonlinear Systems (분리된 비선형 시스템의 적응 퍼지 슬라이딩모드 제어)

  • Kim, Do-U;Yang, Hae-Won;Yun, Ji-Seop
    • 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.

Design of Nonlinear Adaptive Controller using Wavelet Neural Network (웨이브렛 신경회로망을 이용한 비선형 적응 제어기 설계)

  • 정경권;김주웅;엄기환;정성부;김한웅
    • Proceedings of the IEEK Conference
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    • 2001.06c
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    • pp.17-20
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    • 2001
  • In this paper, we design a nonlinear adaptive controller using wavelet neural network. The method proposed in this paper performs for a nonlinear system with unknown parameters, identification with using a wavelet neural network, and then a nonlinear adaptive controller is designed with those identified informations. The advantage of the proposed control method is simple to design a controller for unknown nonlinear systems, because we use the identified informations and design parameters are positioned within a negative real part of s-plane. The simulation results showed the effectiveness of proposed controller design method.

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