Abstract
As the six-phase squirrel cage copper rotor induction motor has some nonlinear characteristics, such as nonlinear friction, nonsymmetric torque, wind stray torque, external load torque, and time-varying uncertainties, better control performances cannot be achieved by utilizing general linear controllers. The snug backstepping control with sliding switching function for controlling the motion of a six-phase squirrel cage copper rotor induction motor drive system is proposed to reduce nonlinear uncertainty effects. However, the previously proposed control results in high chattering on nonlinear system effects and overtorque on matched uncertainties. So as to reduce the immense chattering situation, we then put forward the rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method to estimate the external bundled torque uncertainties and to recoup the smallest reorganized error of the evaluated rule. In the light of Lyapunov stability, the online parametric training method of the rectified reiterative sieved-Pollaczek polynomials neural network can be derived by utilizing an adaptive rule. Moreover, to improve convergence and obtain beneficial learning manifestation, the improved fish school search algorithm is made use of to readjust two fickle learning rates of the weights in the rectified reiterative sieved-Pollaczek polynomials neural network. Lastly, the effectuality of the proposed control system is validated by examination results.
Highlights
A six-phase induction motor [1,2,3] has been broadly applied in various kinds of industrial applications [4,5] because it has higher efficiency, higher reliability, and lower torque ripple in comparison with a three-phase squirrel cage aluminum rotor induction motor
It is only reasonable to expect that all errors defined on the proposed rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method with the indemnified control, the assessed law, and the cozy learning law will converge into a neighborhood with reachable radius and remains within it thereafter, which is so-called uniformly ultimate boundedness
As to reduce the immense chattering situation, we put forward the rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method to estimate the external bundled torque uncertainties and to recoup the smallest reorganized error of the evaluated rule
Summary
A six-phase induction motor [1,2,3] has been broadly applied in various kinds of industrial applications [4,5] because it has higher efficiency, higher reliability, and lower torque ripple in comparison with a three-phase squirrel cage aluminum rotor induction motor. The improved fish school search method is proposed as a novel method to regulate two fickle rates with two optimal values and to raise the convergent speed of weights in this paper Those neural networks need longer calculation time to carry out the nonlinear system. Dynamics control of the proposed rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method for the six-phase squirrel cage copper rotor induction motor drive system is a novel method, and its main aim is to develop the assessed law, to reduce calculation time, and to speed-up the convergence of weight regulation. The main motivation of the proposed rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method is a novel structure in order to reduce calculation complexity that raises the certification property of the nonlinear system because this control construct has faster learning ability and better generalization. This section consists of two subsections that are the models of the six-phase squirrel cage copper rotor induction motor and the conformation of the six-phase squirrel cage copper rotor induction motor drive system
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