Hysteresis system inverse system control method

Abstract

The present invention discloses a method for controlling an inverse system of a hysteretic system, belonging to a nonlinear control method of an inverse system. The method comprises the steps of: extracting input signals, output signals and a local extremum of the output signals from an ascending section and a descending section of the hysteretic system respectively; modeling neural networks of the ascending section and the descending section of the hysteretic system respectively by adopting BP neural networks, taking the extracted output signals and the local extremum of the output signals from the ascending section as target inputs of an ascending section neural network training and the input signals from the ascending section as target outputs of the ascending section neural network training, in a similar way, taking the output signals and the local extremum of the output signals from the descending section as target inputs of a neural network training and the input signals from thedescending section as target outputs of the neural network training; designing the inverse system of the hysteretic system by adopting a switch and connecting the inverse system and the hysteretic system in series to form a pseudolinear system. The pseudolinear system which needs a few of parameters has a simple realization form and a high precision control and is applicable to compensatory piezoelectric drivers, magnetostrictive drivers, shape memory alloys, mechanical clearance hysteresis and the like.

Description

technical field [0001] The invention relates to the application of a neural network in the field of nonlinear control. The neural network is used to establish an inverse model to compensate a hysteresis system, which belongs to the inverse system method of nonlinear control. Background technique [0002] Hysteresis nonlinearity is a widespread scientific phenomenon, such as piezoelectric ceramics, excitation motors, transformers, mechanical gap systems, etc., all have hysteresis phenomena. In the control system, the hysteresis nonlinearity will not only reduce the control precision of the system, but also weaken the feedback function in the closed-loop system, and even cause the instability of the system. Especially in the field of smart materials, smart materials such as piezoelectric ceramics are widely used in precision machining machine tools, electron microscope probe drives, micro-tables, astronomical telescope positioning and other systems due to their high positionin...

AI Technical Summary

Problems solved by technology

In the generalized Maxwell model, the hysteresis system is equivalent to the superposition of several hysteresis units. As long as the number of units is increased, the accuracy can be improved, but the generalized Maxwell model is a symmetric model, which cannot be applied to asymmetric hysteresis systems.

The Preisach model has a simple principle and a wide range of applications, and can describe any complex hysteresis characteristics, but requires too many parameters, requires a huge storage space, requires a large amount of calculation, and the implementation form is relatively complicated, making it difficult to adjust parameters in real time for online control

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