Research on low-speed performance of continuous rotary electro-hydraulic servo motor based on robust control with Adaboost prediction

The Journal of Engineering

Xin Wang¹  Liu M. Zhen²  Sun Y. Wei²  Wang X. Jing² 

[1] School of Mechanical and Aerospace Engineering, Jilin University;School of Mechanical and Power Engineering, Harbin University of Science and Technology;

关键词: control system synthesis;    electrohydraulic control equipment;    hydraulic motors;    adaptive control;    servomotors;    robust control;    servomechanisms;    radial basis function networks;    feedback;    learning (artificial intelligence);    neurocontrollers;    frequency response;    H(∞) theory;    external disturbances;    continuous rotary electro-hydraulic servomotor system;    low speed performance;    system robustness;    real-time control;    system robust control output;    predictive error;    system actual output;    multiple weak neural network learners;    system feedback mechanism;    RBF neural network;    Adaboost algorithm;    generalised state equation;    structured uncertainty;    system mathematic model;    robust control strategy;    nonlinear properties;    parametric perturbation;    dynamic uncertainties;    Adaboost prediction;    low-speed performance;   

DOI  :  10.1049/joe.2018.8970

来源: DOAJ

【 摘 要 】

In order to improve the robustness and low-speed performance of continuous rotary electro-hydraulic servo system under influences of dynamic uncertainties, parametric perturbation, friction, other non-linear properties, and uncertainties, the robust control strategy was proposed with Adaboost prediction. Firstly, basing on the system mathematic model, the model with structured uncertainty and generalised state equation was established with parametric perturbation and external disturbances, and then the robust controller was developed by adopting [inline-formula] theory. Furthermore, Adaboost algorithm based on radial basis function (RBF) neural network was applied to design the system feedback mechanism, so the multiple weak neural network learners were obtained by using Adaboost algorithm to train system actual output and input. Also, these weak neural network learners constituted a strong learner to predict the electro-hydraulic servo system output and calculate the predictive error so as to adjust the system robust control output, so the real-time control was carried out by the robust controller. Some comparative simulated results are obtained to verify the proposed controller guarantees performances of low speed, tracking accuracy, and ability of anti-interference, which greatly expands the band of frequency response and improve the system robustness.

【 授权许可】

Unknown