MRAC control method of hydraulic servo system based on nonlinear neural network

A technology of hydraulic servo system and neural network, applied in the field of MRAC control of hydraulic servo system based on nonlinear neural network, can solve the problems of parameter uncertainty, matching and mismatching interference, achieve good tracking performance, and solve high-gain feedback Effect

Active Publication Date: 2019-12-17
NANJING UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

[0006] The object of the present invention is to provide a MRAC control method for a hydraulic servo system based on a nonlinear neural

Method used

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  • MRAC control method of hydraulic servo system based on nonlinear neural network
  • MRAC control method of hydraulic servo system based on nonlinear neural network
  • MRAC control method of hydraulic servo system based on nonlinear neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0172] The servo valve is Moog G761-3003, the flow rate is 19L / min, the withstand pressure is 7MPa, the frequency range is 120Hz, and the stroke of the double-rod hydraulic chamber is ±44mm. The pressure sensor is MEASUS175-C00002-200BG with an accuracy of 1 Pa and a maximum load of 30 kg. The linear encoder is a Heidenhain LC483 with an accuracy class of micron. The measurement control system consists of display software and real-time control software. The A / D card is Advantech PCI-1716, the D / A card is Advantech PCI-1723, and the reverse card is Heidenhain IK-220. All cards are 16-bit. The sampling time is 0.5 milliseconds. Due to the control method based on model reference, we choose A m for: B m Choose as: Where τ = 0.001 is the time constant.

[0173] The following is a comparison of experimental results. In this embodiment, the following controllers are compared.

[0174] MRNNR: This is the controller designed in this paper. The parameters related to the con...

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Abstract

The invention discloses an MRAC control method of a hydraulic servo system based on a nonlinear neural network. Aiming at matched and unmatched interference and parameter uncertainty in the hydraulicservo system, a nonlinear neural network is adopted to approach state-related interference so as to carry out feed-forward compensation, and meanwhile, parameters related to input are updated on linein order to further improve the accuracy of feed-forward compensation. In the aspect of theoretical proof, the symbolic function robust integral control strategy (RISE) is combined with the MRAC, theapproximation error of the neural network is suppressed through the RISE, asymptotic tracking is realized without utilizing an acceleration signal, and finally the effect of the method is verified through experiments.

Description

technical field [0001] The invention belongs to hydraulic servo system technology, in particular to an MRAC control method of a hydraulic servo system based on a nonlinear neural network. Background technique [0002] For closed-loop control of hydraulic systems, parameter uncertainty and nonlinear disturbances are the main obstacles to achieve high tracking performance. Adaptive control is a good way to mitigate the adverse effects of parameter uncertainty, but it has little effect on nonlinear disturbances. Robust control can improve the robustness to nonlinear disturbances with high-gain feedback, but can cause severe jitter problems. In order to improve the tracking performance of the hydraulic system, the Adaptive Robust Control method (ARC) has been applied to the hydraulic system. However, ARC cannot achieve asymptotic tracking when the system under consideration contains mismatched and / or matched nonlinear disturbances. Based on the Robust Sign Integral of Error (...

Claims

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Application Information

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IPC IPC(8): F15B21/08
CPCF15B21/08
Inventor 姚建勇姚志凯姚飞宇
Owner NANJING UNIV OF SCI & TECH
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