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

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 28/09/2018, Accepted 12/10/2018, Published 19/10/2018

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 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.

Inspec keywords: electrohydraulic control equipment; neurocontrollers; radial basis function networks; servomotors; adaptive control; hydraulic motors; learning (artificial intelligence); servomechanisms; feedback; control system synthesis; robust control

Other keywords: multiple weak neural network learners; low speed performance; system mathematic model; robust control strategy; dynamic uncertainties; low-speed performance; structured uncertainty; parametric perturbation; generalised state equation; system robust control output; RBF neural network; predictive error; Adaboost algorithm; Adaboost prediction; continuous rotary electro-hydraulic servomotor system; system feedback mechanism; frequency response; system robustness; nonlinear properties; external disturbances; real-time control; system actual output; H∞ theory

Subjects: Control system analysis and synthesis methods; Stability in control theory; Neurocontrol; Electrohydraulic and electropneumatic control equipment; Self-adjusting control systems

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