Abstract
The intelligent optimization algorithm has obvious advantages in solving the nonlinear and complexity optimization problems, and is widely used in the optimization of fuzzy control rules. However, it also has the disadvantages of low optimization precision and easy to fall into local optimum. The discrete bat algorithm (DBA) optimizes the fuzzy rules in the fuzzy adaptive PID controller. Firstly, the neighborhood search operator is designed according to the correlation between adaptive fuzzy rules to improve the search accuracy. Secondly, the chaotic mutation operator is used to avoid the algorithm falling into local optimum. Finally, the ITAE integral performance index is used as the optimization objective function and the particle swarm. The algorithm and genetic algorithm optimize the control effect for comparison analysis. The results show that the controller optimized by discrete bat algorithm has advantages in adjusting time and overshoot, and has strong adaptability and robustness.
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Acknowledgments
This work was supported by the Natural Science Foundation of China under grant (No. 61532007, 61370076), the Natural Science Foundation of Jiangsu Province under grant No. 15KJB520001. This work was partly supported by the Natural Science Foundation of Jiangsu Province of China under grant NO. BK2012209, Science and Technology Program of Suzhou in China under grant NO. SYG201409. Finally, the authors would like to thank the anonymous reviewers for their constructive advices.
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Du, X., Zhang, M., Sha, G. (2019). Research on Fuzzy Adaptive PID Fuzzy Rule Optimization Based on Improved Discrete Bat Algorithm. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_67
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DOI: https://doi.org/10.1007/978-3-030-30952-7_67
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