Motor servo system self-adaptive robustness control method including input interval

Abstract

The invention discloses a motor servo system self-adaptive robustness control method including an input interval and belongs to the field of motor servo control. The method comprises the following steps of: establishing a mathematic model and an interval reverse model of an interval-included motor position servo system; establishing a self-adaptive robustness controller based on the interval reverse model; and utilizing a Lyapunov stability theory to carry out stability demonstration on the interval-included motor position servo system. The controller designed in the invention fully considers and compensates friction and external interference non-linearity, and the designed interval reverse model effectively removes the influences of interval non-linearity on the system. The designed controller comprises a self-adaptive item, the model error caused by parameter changes can be effectively processed, and the control performance is improved.

Description

technical field [0001] The invention relates to the field of motor position servo systems, in particular to an adaptive robust control method for a motor servo system with an input gap. Background technique [0002] Gap nonlinearity widely exists in various mechanical systems, in short, it is the interaction between the driving part and the driven part. There can be linear motion or rotary motion between them. When the system needs to change direction or brake, the existence of gap will seriously damage the transmission performance of the mechanical system. Generally speaking, the operation modes of mechanical systems with gaps can be divided into "gap mode" and "contact mode". The gap mode means that the driving part and the driven part are in a non-contact state. Contact mode means that two parts are in contact with each other and there is a transfer of torque between them. As the system operates, the inherent switching of the two modes makes the system a typical hybrid...

AI Technical Summary

Problems solved by technology

A large number of literatures use continuous or discontinuous adaptive inverse methods to perform gap compensation, but this method has very strict restrictions on initial conditions

At the same time, using neural network for static and dynamic compensation to improve the tracking performance of nonlinear systems with gaps is also a research hotspot for scholars, but this gap compensation method based on neural networks or fuzzy control requires a large amount of calculation.

However, some literatures propose to use optimal control to solve the gap nonlinear collision problem, which requires a large online computing time similar to neural network optimization.

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