Model uncertainty mechanical arm motion control method based on multi-layer neural network

Abstract

The invention provides a model uncertainty mechanical arm motion control method based on a multi-layer neural network. Firstly, controller design is performed on mechanical arm system model uncertainty according to a nominal model; a mechanical arm system dynamic model with uncertainty is established; a mechanical arm system nominal model is established with an uncertain item caused by external interference factors being considered; a controller is designed for the model uncertain item according to the normal model; the multi-layer neural network is adopted to perform self-adaptive approach onthe model uncertain item; and a mechanical arm system controller is designed based on the multi-layer neural network. According to the model uncertainty mechanical arm motion control method based onthe multi-layer neural network, a good robustness effect on structural uncertainty such as parameters and non-structural uncertainty such as external interference is achieved simultaneously, and it can be guaranteed that the track of the tail end of a mechanical arm and the angles of all joints are well tracked.

Description

technical field [0001] The invention belongs to the field of manipulator control, in particular to a model uncertainty manipulator motion control method based on a multilayer neural network. Background technique [0002] As a mechatronic device, the robotic arm can efficiently complete various complex and dangerous operations, improve production efficiency, and is widely used in industry and daily life. The rapid development of this field in recent years has put forward higher requirements for the high-precision motion control of the robotic arm. However, as a complex nonlinear system, the manipulator system has structural and non-structural uncertainties, such as unmodeled disturbances, nonlinear friction, parameter uncertainties, external disturbances, etc. The existence of these uncertainties has a great impact on the motion control accuracy of the manipulator, thus increasing the difficulty of controller design. [0003] For the motion control of the manipulator, commo...

AI Technical Summary

Problems solved by technology

Due to the large nonlinear feedback gain often leads to the conservatism of the design (that is, high-gain feedback), it is difficult to use it in engineering

However, when the non-structural uncertainties such as external disturbances gradually increase, the designed adaptive robust controller will cause the tracking performance to deteriorate and even become unstable.

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