Multi-model-based servo system adaptive control system

Abstract

The present invention discloses a multi-model-based servo system adaptive control system which belongs to the electromechanical control field. The system comprises a multi-mode parameter estimation module, a switching mechanism module and an adaptive controller, wherein a servo system is controlled by the adaptive control system. The multi-mode parameter estimation module comprises n fixing modules, an identification model and an adaptive model, and each module in the multi-mode parameter estimation module outputs a set of model parameters aiming at the servo system and inputs to the switching mechanism module. The switching mechanism module selects a set of model parameters making a cost function to be minimum from the multiple sets of model parameters as the optimal model parameters, and inputs to the adaptive controller, and the adaptive controller carriers out the adaptive control law calculation on the inputted optimal model parameters to obtain an adaptive controlled quantity, and inputs to the controlled object. The system can obtain the estimation parameters which can represent the system dynamic characteristics best without needing to obtain the priori knowledge about the system uncertainty.

Description

technical field [0001] The invention relates to a DC motor servo control system, which belongs to the field of electromechanical control, and in particular to a multi-model-based servo system self-adaptive control system. Background technique [0002] In servo system control, due to the existence of uncertain factors such as dynamic friction, load changes and external disturbances, the parameters of the system model often jump, which has a great impact on control performance such as response speed, precision and stability. In addition, if there are unknown parameters in the control system, the initial estimates of these parameters may differ greatly from their true values. Therefore, if the parameters of the system model cannot be obtained accurately during the control process, it is difficult to achieve the ideal control effect. [0003] Traditional adaptive methods can estimate parameters in real time through adaptive laws to achieve fast tracking. However, in a complex ...

AI Technical Summary

Problems solved by technology

However, in a complex environment, sudden changes in parameters will greatly lengthen the time for adaptive adjustment, resulting in deterioration of transient performance

Increasing the adaptive proportional gain can speed up the convergence of parameters, but at the same time it will also increase the sensitivity of the system to noise, making the steady-state performance worse

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