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Bridge crane model prediction control method based on data driving

A technology of model predictive control and bridge cranes, applied in the direction of adaptive control, general control system, control/regulation system, etc., to achieve the effect of fast and accurate positioning, high application value, and anti-swing control goals

Pending Publication Date: 2022-05-13
无锡安起科技有限公司
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Problems solved by technology

The prior art also lacks a method to find the optimal controller prediction model and parameters

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  • Bridge crane model prediction control method based on data driving
  • Bridge crane model prediction control method based on data driving
  • Bridge crane model prediction control method based on data driving

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Embodiment Construction

[0056] The present invention will be further described below in conjunction with the accompanying drawings, but the present invention is not limited in any way. Any transformation or replacement based on the teaching of the present invention belongs to the protection scope of the present invention.

[0057] The overall structure of the bridge crane includes two parts: trolley operating mechanism and lifting mechanism. The trolley running mechanism can be divided into two parts: a large trolley and a small trolley. The trajectories of the small car and the big car are perpendicular to each other. The trolley is responsible for translating the entire overhead crane along the elevated rails on both sides of the production line. It is responsible for the translation on the bridge, and the load lifting mechanism is responsible for completing the lifting movement of the load. According to the crane industry specification, a complete crane operation process generally includes the f...

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Abstract

The invention discloses a bridge crane model prediction control method based on data driving. The method comprises the following steps: step 1, determining a controlled variable and a control variable of a system, designing a model prediction controller, parameterizing the controller, designing a performance index, inputting and outputting historical data through an acquisition system, and initializing a database; 2, training a Gaussian process model according to the database to simulate dynamic characteristics of a crane system, designing an operation experiment, and performing the experiment to obtain state data of the system; 3, collecting input and output data through a sensor, updating a historical database of the system, returning to the step 2 until a termination condition is met, and entering the step 4; and 4, continuously learning data to obtain parameters meeting the optimal control performance as a final actual control predictive control model. According to the invention, parameter setting is carried out by using experimental data, a prediction model with excellent performance is learned, and rapid and accurate positioning and anti-swing control targets of the trolley are realized.

Description

technical field [0001] The invention belongs to the technical field of crane self-adaptive control, in particular to a data-driven bridge crane model predictive control method. Background technique [0002] As a typical underactuated manipulator system, overhead crane is an important means of cargo transportation. It is widely used in construction sites, ports, production workshops, warehouses and other industrial fields. Its main control objectives can be summarized as accurately transporting goods to target locations and suppressing load swing as much as possible. The swing angle of the payload must be small enough to avoid accidents during transport. Suppose an overhead crane swings too far. Affect its operation safety and production efficiency. Due to the lack of some actuators, the swing angle of its payload is underactuated, making the swing angle difficult to control. How to suppress the swing of the payload and ensure the accurate positioning of the trolley is a...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 许敏赵付田朱丽
Owner 无锡安起科技有限公司
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