Open AccessNONLINEAR DYNAMIC CALIBRATION AND CORRECTION OF ACCELERATION SENSOR BASED ON ADAPTIVE NEURAL NETWORK
Author(s) -
Shuo Xiao,
Shengzhi Wang,
Jiayu Zhuang,
Zhenzhen Huang,
Guopeng Zhang
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400989
Subject(s) - acceleration , nonlinear system , control theory (sociology) , artificial neural network , computer science , noise (video) , compensation (psychology) , sensitivity (control systems) , range (aeronautics) , dynamic range , component (thermodynamics) , engineering , electronic engineering , artificial intelligence , physics , control (management) , classical mechanics , quantum mechanics , psychology , psychoanalysis , image (mathematics) , computer vision , thermodynamics , aerospace engineering
With the development of industrial technology, acceleration sensors have a wide range of applications. The application environment has strict requirements on acceleration sensor, which needs it to get accurate input and output response in complexity. According to the Hammerstein model, this paper studies the dynamic nonlinear relationship between the output voltage and the input acceleration of the acceleration sensor that can be divided into static nonlinear component and dynamic linear component. We combine the least square method with adaptive neural network to calculate the parameters of static nonlinear and dynamic linear components. The least square method is used to improve the training performance of the network and avoid the network falling into the local minimum of the traditional neural network. Experimental results show that compared with other methods, the proposed method has the advantages of less training steps and strong approximation ability, and the algorithm is less affected by external noise. This method can realize nonlinear system identification of acceleration sensor and provide reliable basis for compensation.