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The accuracy of contour tracking is an important issue to be addressed for the X‐Y stage in a machining tool. Applying a better control scheme can help to increase the tracking accuracy with the effects of systems' uncertainties, nonlinearities and disturbances. In this paper, to prevent these negative effects and achieve high accuracy tracking control of the two‐axis motion stage, the adaptive fuzzy wavelet neural network (AFWNN) PI sliding mode controller is proposed. The adaptive algorithm is combined with the basis function of the wavelet neural network and fuzzy system to approximate the plant model functions. Then, the controller with PI sliding mode structure could be built based on the estimated system function. In this method, the time domain positioning property of wavelet and the reasoning of the fuzzy system could provide the well‐estimated plant model. Moreover, the adaptive algorithm and sliding mode control scheme help to reduce the effects of parameter variation and uncertainties. The stability is analyzed via Lyapunov stability theorem to prove the asymptotic stability of the system. In the simulation and experimental results, the contours are planned through the nonuniform rational B‐spline (NURBS), which is considered a high accuracy interpolator. The tested curves consist of circle, bow, heart, star and trident. The average and standard deviations of the tracking errors are compared to other methods to illustrate the performance of the proposed method. The results demonstrate that the method can reduce errors more effectively and have better localization for trajectory tracking. [ABSTRACT FROM AUTHOR]

The accuracy of contour tracking is an important issue to be addressed for the X‐Y stage in a machining tool. Applying a better control scheme can help to increase the tracking accuracy with the effects of systems' uncertainties, nonlinearities and disturbances. In this paper, to prevent these negative effects and achieve high accuracy tracking control of the two‐axis motion stage, the adaptive fuzzy wavelet neural network (AFWNN) PI sliding mode controller is proposed. The adaptive algorithm is combined with the basis function of the wavelet neural network and fuzzy system to approximate the plant model functions. Then, the controller with PI sliding mode structure could be built based on the estimated system function. In this method, the time domain positioning property of wavelet and the reasoning of the fuzzy system could provide the well‐estimated plant model. Moreover, the adaptive algorithm and sliding mode control scheme help to reduce the effects of parameter variation and uncertainties. The stability is analyzed via Lyapunov stability theorem to prove the asymptotic stability of the system. In the simulation and experimental results, the contours are planned through the nonuniform rational B‐spline (NURBS), which is considered a high accuracy interpolator. The tested curves consist of circle, bow, heart, star and trident. The average and standard deviations of the tracking errors are compared to other methods to illustrate the performance of the proposed method. The results demonstrate that the method can reduce errors more effectively and have better localization for trajectory tracking. [ABSTRACT FROM AUTHOR]

This paper proposes a reference model adaptive fuzzy Hermite neural-network controller (RAFHNC) for position tracking of a modeless magnetic bearing system (MBS). Magnetic bearings evolved from traditional bearings and can eliminate the shortcomings of traditional bearings by using electromagnetic forces to reduce friction during use. The MBS is nonlinear, for which system parameters are difficult to obtain accurately. This paper proposes the RAFHNC for precision position tracking in an MBS. The RAFHNC has three important characteristics. First, the RAFHNC does not require the MBS parameters to track a reference model accurately. Second, the RAFHNC uses Hermite functions to replace the Gaussian function, this reduces training time in the membership level. Third, the RAFHNC uses an online self-tuning fuzzy Hermite neural network estimator to resolve lumped uncertainty in the MBS. The fuzzy Hermite neural network incorporates expert knowledge, reducing training time on controller parameters. Finally, the Lyapunov stability and system adaptive laws are used to guarantee convergence. The output responses of the RAFHNC and adaptive reference model sliding-mode controller (ARSC) methods were compared. The RAFHNC method exhibited improved output responses compared with the ARSC method in terms of root mean squared error (RMSE) index. It can reduce problems associated with external loads and system lumped uncertainty in MBS. [ABSTRACT FROM AUTHOR]