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A modified aquila optimizer with wide plant adaptability for the tuning of optimal fractional proportional–integral–derivative controller

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Abstract

The heuristic tuning method of fractional-order proportional–integral–derivative (FOPID) control systems lacks robustness, and its performance often changes with specific controlled plants. To solve this problem, this paper proposes a new modified aquila optimizer for tuning the parameters of the FOPID controller, which has strong plant adaptability and can be applied to a large class of different controlled plants. A series of new operational mechanisms, including Tent map-based initialization, probability-based dynamic update, greedy-based Gauss mutation, a fully random search strategy with uniform distribution, and the wraparound dynamic weight update for local exploitation, are proposed to tackle the existing problems of the classical aquila optimizer, such as slow convergence, low precision, and local optimum. Standard benchmark functions are used to test the proposed modified aquila optimizer, showing superior performance in terms of convergence speed, precision, and robustness. The Wilcoxon and Friedman tests statistically proved the significant difference of the modified aquila optimizer from other competitors. Five control system cases with different plants further validate the effectiveness, feasibility, wide adaptability, and superiority of the proposed modified aquila optimizer for regulating FOPID controller parameters. It is approved that the proposed modified aquila optimizer with new mechanisms has wide plant adaptability, deeming a good prospect for the tuning of optimal FOPID controller.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work is supported by the Key Technologies R&D Program of Sichuan Province, China under Grant No. 23ZDYF0471 and by the Doctoral Research Fund of Southwest University of Science and Technology under Grant No. 22zx7140. The authors also thank the anonymous reviewers for their insightful and constructive comments.

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Correspondence to Geng Wang.

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Appendix

Appendix

See Tables 26, 27 and 28.

Table 26 The unimodal benchmark functions
Table 27 The high-dimensional multimodal benchmark functions
Table 28 The fixed-dimensional multimodal benchmark functions

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Ni, L., Li, Y., Zhang, L. et al. A modified aquila optimizer with wide plant adaptability for the tuning of optimal fractional proportional–integral–derivative controller. Soft Comput (2023). https://doi.org/10.1007/s00500-023-09473-2

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  • DOI: https://doi.org/10.1007/s00500-023-09473-2

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