Improved linear optimal semi-active control method

A semi-active control and optimal technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problem that the weighting function of the active controller is difficult to determine, so as to make up for the lack of control effect and improve the accuracy of current prediction , Enhance the effect of vibration reduction effect

Active Publication Date: 2019-05-07
MINJIANG UNIV
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Problems solved by technology

[0006] In view of this, the purpose of this invention is to propose an improved linear optimal semi-active control method, which introduces the crow search algorithm and the imperial competition algorithm into the semi-active controller design, so that the parameters of the LQR controller and the ANFIS reverse model

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[0065] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0066] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0067] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combina...

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Abstract

The invention relates to an improved linear optimal semi-active control method. Since damper has high nonlinearity, control of the damping force by adjusting the control current or voltage is required. Therefore, the control method is composed of two levels of controllers, namely, an improved linear optimal controller (LQR) and an adaptive neuro-fuzzy inference system (ANFIS) inverse model. The control method comprises the following steps: firstly, performing optimal design on the LQR controller by a crow search algorithm (CSA); then calculating desired ideal control force for vibration reduction by using the optimal LQR controller; and finally, improve an ANFIS by using the ideal control force as the input of an ANFIS inverse model with a hybrid training algorithm based on an imperial competition algorithm (ICA) in order to output a control signal of a magnetorheological damper accurately. The method can improve the current prediction accuracy of the MR damper, and make up for the deficiency of the control effect of the existing linear optimal semi-active control, thereby enhancing the vibration reduction effect of the MR damper.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and semi-active control, in particular to an improved linear optimal semi-active control method. Background technique [0002] The magnetorheological damper is an intelligent semi-active control device, which has many advantages such as high failure safety, fast response, large output, and low energy consumption. However, because the damping force has obvious hysteresis characteristics and complex nonlinearity, the damping force can only be controlled indirectly by controlling the current or voltage of the damper. Aiming at the shock absorption requirements of building structures based on magnetorheological dampers, in order to make full use of the excellent damping characteristics of magnetorheological dampers, it is necessary to conduct in-depth research on semi-active control algorithms based on magnetorheological dampers. [0003] Linear quadratic optimal control (LQR) is a kind of opt...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 林秀芳
Owner MINJIANG UNIV
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