A rbf neural network adaptive control method for multi-single-arm manipulators

Abstract

The invention discloses an RBF neural network self-adaptive control method for a multi-single-arm manipulator, which utilizes the neural network to approach the unknown nonlinear function in the manipulator system; introduces dynamic surface technology to design a first-order filter to solve the need for backstepping to the controller The problem of "computational explosion" caused by repeated derivation; for the problem of unknown parameters and limited output, Nussbaum function and obstacle Lyapunov function are used to deal with it. The present invention does not require an accurate dynamic model of the manipulator, can completely eliminate output errors caused by unknown dynamic parameters and random disturbances, and makes up for the problem that the model-based multi-single-arm manipulator control scheme cannot do without an accurate dynamic model. The dynamic performance of the manipulator and the trajectory tracking accuracy in the joint space are improved. Finally, the feasibility and effectiveness of the control method are verified by a simulation example.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence and control, in particular to an RBF neural network adaptive control method for multi-single-arm manipulators with limited output, unknown parameters and random interference. Background technique [0002] With the continuous development of science and technology, multi-single-arm manipulators appear as a powerful tool in modern assembly line production work. The single-arm manipulator has the limitations of poor flexibility and low efficiency. In the relatively large handling, installation, maintenance and welding work, the single-arm manipulator has shown certain limitations in some aspects such as information collection, control and processing, and Because multi-single-arm manipulators have strong coordination operation flexibility and large load capacity, they can complete tedious and diverse task requirements. Therefore, it is of great significance to study multi-single-arm man...

AI Technical Summary

Problems solved by technology

The single-arm manipulator has the limitations of poor flexibility and low efficiency. In the relatively large handling, installation, maintenance and welding work, the single-arm manipulator has shown certain limitations in some aspects such as information collection, control and processing, and Because multi-single-arm manipulators have strong coordination operation flexibility and large load capacity, they can complete tedious and diverse task requirements. Therefore, it is of great significance to study multi-single-arm manipulator cooperation systems, especially in multi-agent systems. The design of a consistent output controller for a single-arm manipulator is of great significance, however, its relatively cumbersome control problem is a difficult problem

[0004] For systems with uncertain nonlinear terms, the adaptive neural network can approximate the unknown nonlinear function, and obtain the expression form of the final control input through backstepping control, but backstepping control has the problem of computational complexity explosion

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