Partial saturation adaptive controller of bridge crane, control system and control method

Abstract

The invention discloses a partial saturation adaptive controller of a bridge crane, a control system and a control method. The control method comprises the following steps: wx online estimation w(^)x and wl online estimation w(^)l are input to a neural network for dynamic learning; w(^)x and w(^)l' undergoes dynamic output by the neural network and are transmitted to the partial saturation adaptive controller; and finally the partial saturation adaptive controller outputs driving force which can guarantee smooth start-up of the bridge crane system, wherein wx and wl are both auxiliary vectors. By introducing hyperbolic tangent function into the controller, initial driving force is greatly reduced, and smooth start-up of the bridge crane can be guaranteed.

Description

technical field [0001] The invention belongs to the field of automatic control, and in particular relates to a local saturation adaptive controller, a control system and a control method of an overhead crane. Background technique [0002] Underactuated electromechanical systems include cranes, inertial pendulum systems, flexible systems, underactuated robots, underactuated vehicles, etc., and have the advantages of simple structure, low energy consumption, low cost, light weight, and high system flexibility. However, due to the high nonlinearity of the system and the small amount of control, it brings great challenges to its control. As a typical underactuated electromechanical system, overhead cranes have been widely used in construction sites, seaports, docks, production lines and other occasions to complete the transportation of goods, and its main goal is fast and accurate load positioning. Generally speaking, a complete crane operation process mainly includes the follo...

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Problems solved by technology

[0004] Currently, most of the control methods for the accompanying load lifting motion cannot guarantee the smooth start-up of the overhead crane

Due to the complex working environment of the overhead crane system, the overhead crane system is usually affected by uncertain factors of system parameters such as load mass, overhead crane mass, suspension rope length, friction force, and external disturbances such as air resistance. It is difficult to measure these system parameters such as the mass of the crane, the length of the suspension rope, and the friction, as well as external disturbances.

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