Novel study on active reliable PID controller design based on probability density evolution method and interval-oriented sequential optimization strategy

Liu, Jiaxiang, Wang, Lei and Wu, Zhangming ORCID: https://orcid.org/0000-0001-7100-3282 2024. Novel study on active reliable PID controller design based on probability density evolution method and interval-oriented sequential optimization strategy. Computer Methods in Applied Mechanics and Engineering 427 , 117057. 10.1016/j.cma.2024.117057 Item availability restricted.

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Abstract

Vibration active control is a significant problem in practical engineering, but the influence of hybrid uncertainties in practical engineering cannot be ignored. For the uncertainties with adequate data, probability theory can be used while for the uncertainties with insufficient data, probability theory is limited and interval quantification can be used. Taking the widely used proportional-integral-differential (PID) control as an example, a novel study on controller design based on probability density evolution method and interval-oriented sequential optimization strategy considering interval and random uncertainties is proposed. Firstly, under a certain sample point of interval uncertainties, probability density evolution equation is utilized to calculate the probability density function of uncertain response considering only random uncertainties. In this way the probabilistic reliability corresponding to the sample point can be obtained. Then the hybrid reliability is defined as the average probability within the interval uncertainty and the adaptive dichotomy method is utilized to choose proper sample points of interval uncertainties, in which the reliability curve and the interval uncertainty axis is regarded as the judgement of convergence. Next the hybrid reliability is introduced to the optimization as a constraint and the PID gains reliable design optimization is established to balance the energy-consuming and safety. Finally, the sequential optimization strategy is adopted to improve the efficiency of optimization. In every iteration step of the optimization, the constraint of deterministic optimal design is adjusted according to the static reliability at the moment before the passage or reaching the maximum value. Three numerical examples are presented to demonstrate the accuracy and practicability of the proposed framework.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0045-7825
Date of First Compliant Deposit:	10 July 2024
Date of Acceptance:	9 May 2024
Last Modified:	09 Nov 2024 12:30
URI:	https://orca.cardiff.ac.uk/id/eprint/169289

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