Self-adaptive inverse tracking control method for super-magnetostrictive tracking platform

Abstract

The invention discloses a self-adaptive inverse tracking control method for a super-magnetostrictive tracking platform. The self-adaptive inverse tracking control method comprises the following stepsthat firstly, a left inverse model of a super-magnetostrictive actuator is obtained through offline identification; secondly, based on a Filtered-epsilon LMS algorithm, two completely identical nonlinear filters are used for copying the left inverse model of the super-magnetostrictive actuator; finally, an error signal obtained by output subtraction of the two nonlinear filters is utilized to finda controller in a self-adaptive mode, the weight coefficients of the filters are adjusted online by adopting the LMS algorithm till the output of the super-magnetostrictive actuator is identical to the output of a reference model, wherein the input of one of the two completely identical nonlinear filters is an object output, and the input of the other nonlinear filter is the output of the reference model. By offsetting the delaying portion of the super-magnetostrictive actuator, accurate tracking control of the super-magnetostrictive actuator is achieved.

Description

technical field [0001] The invention relates to an adaptive inverse tracking control method for a giant magnetostrictive tracking platform, belonging to the field of dynamic hysteresis nonlinear system modeling and control. Background technique [0002] Smart materials commonly used in modern industry include piezoelectricity, giant magnetostriction and shape memory alloys. They exhibit coupling characteristics of electricity, heat, magnetism, and force fields, which can be used to design them as actuators or sensors. For positioning tracking of micron-scale displacement, giant magnetostrictive materials are usually used. However, this smart material has serious dynamic hysteresis nonlinear characteristics. The nonlinear characteristic of dynamic hysteresis not only reduces the control precision of the control system, but also reduces the stability of the closed-loop system and even causes the system to oscillate. [0003] Adaptive inverse control uses the inverse of the ...

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Problems solved by technology

[0004] The existing linear adaptive inverse control methods mainly include Filtered-X LMS algorithm and Filtered-ε LMS algorithm. However, the above-mentioned linear adaptive filter-based control algorithm will encounter great difficulties in the face of dynamic hysteresis nonlinear systems

Specifically, for the Filtered-X LMS algorithm, the problem is that the product of the left inverse of the system and the system is not equal to the product of the right inverse of the system and the system, so it cannot be applied to nonlinear system control; for the Filtered-ε LMS algorithm , the adaptive inverse controller of the linear plant approaches the reciprocal of the transfer function of the plant. However, for the nonlinear plant, the transfer function often does not exist, so the Filtered-ε LMS algorithm cannot be directly applied to the nonlinear system In control (that is, it cannot be directly applied to the giant magnetostrictive tracking platform)

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