Robust preset performance control method for distributed mechanical arm system

Abstract

The invention discloses a robust preset performance control method for a distributed mechanical arm system. The robust preset performance control method comprises the following steps of 1, establishing a mathematical model of the distributed mechanical arm system; 2, designing a preset performance constraint; and 3, designing a distributed robust preset performance controller. The control method does not depend on a nonlinear model and is low in complexity, angle stability and tracing control of multiple mechanical arm joints under unknown nonlinearity and external interference are achieved, and tracing control of transient and steady-state performance of a master-slave mechanical system is guaranteed by the master-slave mechanical system; according to the preset performance control method, the trajectory tracking control problem of a single mechanical system can be solved, meanwhile, the cooperative control problem of multiple mechanical systems can also be solved, application objectsof the preset performance control method are further enriched, and a solid theory and method foundation are provided for solving more actual mechanical system control problems.

Description

technical field [0001] The invention relates to a method for controlling robust preset performance of a distributed mechanical arm system, belonging to the technical field of mechanical system control. Background technique [0002] In practical engineering, it is often necessary for multiple mechanical systems with simple structures to complete a complex task. On the one hand, the cooperative work of multiple mechanical systems can greatly improve the degree of freedom and reliability of the system, and on the other hand, it is also beneficial to reduce the cost of the controlled system. [0003] There are many research results on distributed control of multiple mechanical systems, such as finite-time distributed control method based on sliding mode control technology, adaptive distributed control based on neural network, distributed control based on event drive, etc. In order to realize the preset of the transient and steady-state performance of the distributed control sys...

AI Technical Summary

Problems solved by technology

Although the above controls are effective, there are two limitations: First, the use of neural networks will inevitably involve complex weight parameter adjustment issues. When the number of layers and nodes of the neural network increases, the problem will become more complicated and require a lot of effort. Computing resources are not conducive to the online use of control laws; secondly, the approximate nonlinearity of the neural network is only valid within the initially defined compact set range, how to ensure the effectiveness of network learning is a difficult problem to solve

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