Neural network active-disturbance-rejection controller for AC radial magnetic bearing, and construction method thereof

Abstract

The invention discloses a neural network active-disturbance-rejection controller for an AC radial magnetic bearing, and a construction method thereof. The input of a first tracking differentiator is given radial displacement x*, and the output is a tracking signal x1 and a differential signal x2; the input of a first self-adaptive expanded state observer is controlling quantity u, radial displacement x and three parameters beta01, beta02 and beta03, the other two inputs of a first nonlinear state error feedback control rule is parameters beta1 and beta2, and the output is the controlling quantity u0; the difference of the controlling quantity u0 and an estimation value z3 is the input of a first compensation factor, the output of a second compensation factor is the controlling quantity u,and the controlling quantity u is used as one input of a first self-adaptive active-disturbance-rejection controller. By constructing the self-adaptive extended state observer, the internal disturbance and the externa disturbance of the controlled object are automatically controlled, and the online automatic adjusting of the three parameters beta01, beta02 and beta03 can be realized along the system disturbance change, the estimation and compensation precision on the disturbance by the extended state observer are increased, and the control performance of the active-disturbance-rejection controller is improved.

Description

technical field [0001] The invention belongs to the technical field of electric drive control equipment, and relates to the decoupling control technology of AC radial magnetic bearings, in particular to an adaptive active disturbance rejection controller for AC radial magnetic bearings based on neural networks, which is suitable for multivariable, nonlinear, Decoupled control of strongly coupled AC radial magnetic bearings. Background technique [0002] Magnetic bearing is a kind of high-performance non-contact bearing that uses magnetic field force to make the rotor stably levitate and has no contact with the stator. It has long life, low loss, no friction, and no lubrication. , high precision and high speed advantages. The radial magnetic bearing system is a strongly coupled, nonlinear multiple-input and multiple-output system. In order to achieve high-speed, high-precision and stable operation of the magnetic bearing, it is necessary to perform linear decoupling control ...

AI Technical Summary

Problems solved by technology

The Chinese patent publication number is CN107037729, and the document titled "A Design Method of Active Disturbance Rejection Controller Based on RBF Neural Network" proposes an RBF-based ADRC controller, which uses the RBF neural network to only One parameter of the nonlinear feedback control law of the first-order ADRC controller is adjusted, and other key parameters of the ADRC controller are not adjusted

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