OS-ELM based modeling and control method and system of piezoelectric ceramic driver

Abstract

The invention discloses an OS-ELM based modeling and control method and system of a piezoelectric ceramic driver. According to the invention, currently expected output displacement, previous multiple output displacement and input driving voltage are used as model input values, and the input driving voltage values at the present are used as model output values, so a hysteresis nonlinearity multi-valued mapping problem is solved, and the output values can be directly used for driving the piezoelectric ceramic driver so that tedious model inversion solving processes are avoided; then, a weight and a threshold value between a hidden layer and an input layer are randomly given, the model is converted into a linear model, and a connection weight between the hidden layer and the output layer can be further calculated only by adopting the simple generalized inverse calculation, so training time is greatly shortened; an infinite differentiable function is used as an activation function of the hidden layer, so quite high precision is achieved; and furthermore, online adaptive updating of parameters can be achieved only by using the recursive least squares method, thereby facilitating improving of applicable performance of the model.

Description

technical field [0001] The invention relates to the technical field of precision control, in particular to an OS-ELM-based piezoelectric ceramic driver modeling and control method and system. Background technique [0002] Piezoelectric ceramic actuators have the advantages of small size, light weight, high rigidity, large output force, high output displacement resolution and fast dynamic response, and are widely used in the field of high-precision machining control. But actuators made of piezoelectric materials are inherently hysteretic nonlinear. In a precision drive system, solving the control error problem caused by the hysteresis nonlinear characteristic is a very challenging problem, and it is also a key problem to improve the control accuracy. [0003] At present, there are three main categories of hysteretic nonlinear modeling methods: physical (such as Maxwell model, Duhem model, and JA model), semi-physical (such as preisach model, PI model) and intelligent (such a...

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Problems solved by technology

[0004] These models either have low precision and slow modeling speed, or it is difficult to model online or the inverse model is difficult to obtain; and almost none of them can be applied in a well-combined control, and most hysteretic nonlinear models must rely on Some other control links are used, such as PID control, repetitive control, etc.; even if there are a few hysteretic nonlinear models that can realize motion control, it is difficult for these models to perform effective online adaptive parameter adjustment

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