Abstract
In light of fine learning ability in the existing uncertainties, a sage revised reiterative even Zernike polynomials neural network (SRREZPNN) control with modified fish school search (MFSS) method is proposed to control the six-phase squirrel cage copper rotor induction motor (SSCCRIM) impelled continuously variable transmission assembled system for obtaining the brilliant control performance. This control construction can carry out the SRREZPNN control with the cozy learning law, and the indemnified control with an assessed law. In accordance with the Lyapunov stability theorem, the cozy learning law in the revised reiterative even Zernike polynomials neural network (RREZPNN) control can be extracted, and the assessed law of the indemnified control can be elicited. Besides, the MFSS can find two optimal values to adjust two learning rates with raising convergence. In comparison, experimental results are compared to some control systems and are expressed to confirm that the proposed control system can realize fine control performance.
Highlights
Artificial intelligent systems have been widely used in many commercial and industrial applications
Thereby, the even Zernike polynomials (EZPs) that are orthogonal on the unit disk found in the extended Nijboer–Zernike theory of diffraction and aberrations [9] with a sequence of polynomials combined with neural networks (NNs) are not yet proposed in modellings, estimations, predictions and controls for nonlinear systems
Impelled continuously variable transmission assembled system with nonlinear uncertainties have been successfully reduced by the four simplified dynamic models
Summary
Artificial intelligent systems have been widely used in many commercial and industrial applications. Artificial neural networks (ANNs) [1,2,3,4] were one of the popular methods in modeling, control, estimation and prediction of nonlinear systems with better learning ability. A lot of orthogonal polynomials neural networks (NNs) [5,6,7,8] were proposed to apply to various kinds of modellings, identifies, approximations and controls of nonlinear systems because of faster computing ability. These NNs combined with some controllers have not appeared to have any adjustable mechanisms of weights. The feedforward even Zernike polynomials neural network (FEZPNN) may not be able to approximate nonlinear dynamic uncertainties effectively in light of lacking reiterative loop
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