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Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(2): 618-627
Jianming Wei, Youan Zhang and Hu Bao, "An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 618-627, Mar. 2018. doi: 10.1109/JAS.2017.7510361
Citation: Jianming Wei, Youan Zhang and Hu Bao, "An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 618-627, Mar. 2018. doi: 10.1109/JAS.2017.7510361
shu

An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems

doi: 10.1109/JAS.2017.7510361
  • 1.

    School of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China

  • 2.

    College of Engineering, Yantai Nanshan University, Yantai 265713, China

  • 3.

    Aeronautical College, Yantai Nanshan University, Yantai 265713, China

Funds:

the National Natural Science Foundation of China 60674090

Shandong Natural Science Foundation ZR2017QF016

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    Abstract
  • This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control (AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance. To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control (ILC), a new boundary layer function is proposed by employing Mittag-Leffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function (CEF) containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

     

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