MFAC parameter optimization based on improved sparrow search algorithm

Abstract

Model-free adaptive control (MFAC) is based on data and does not depend on the mathematical model of the controlled object. It is simple in structure and easy to implement. At present, there are few methods to determine the parameters of MFAC controller, which brings considerable inconvenience to the research and application. Therefore, an improved sparrow search algorithm (ISSA) is proposed to optimize the parameters of MFAC. The ISSA improves its performance by introducing the piecewise chaotic map operator, and finally realizes the automatic optimization of MFAC. The results show that the ISSA has better searching speed and precision. After using optimized parameters for control, the overshoot is greatly reduced, and the oscillation phenomenon when the desired output changes or the external disturbance occurs is effectively overcome, which makes the system more robust.

Complex Theory and Batch Processing in Mechanical Systemic Data Extraction

“…The foraging and anti-predatory behavior of sparrows inspires the SSA. The algorithm has the advantages of a robust optimization ability and a faster convergence speed [27]. Experiments demonstrate that the diagnosis model based on the SSA or the optimized SSA improves the fault diagnosis accuracy of the mechanical system [28]- [32].…”