A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles
In this study, a hybrid heading control framework for unmanned surface vehicles (USVs) is proposed, combining variable domain fuzzy Proportional–Integral–Derivative (VUF-PID) with an improved algorithmic Beetle Antennae Search–Particle Swarm Optimization–Simulated Annealing (BAS-PSO-SA) optimization...
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MDPI AG
2025-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/558 |
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| author | Huixia Zhang Zhao Zhao Yuchen Wei Yitong Liu Wenyang Wu |
| author_facet | Huixia Zhang Zhao Zhao Yuchen Wei Yitong Liu Wenyang Wu |
| author_sort | Huixia Zhang |
| collection | DOAJ |
| description | In this study, a hybrid heading control framework for unmanned surface vehicles (USVs) is proposed, combining variable domain fuzzy Proportional–Integral–Derivative (VUF-PID) with an improved algorithmic Beetle Antennae Search–Particle Swarm Optimization–Simulated Annealing (BAS-PSO-SA) optimization to address the multi-objective control challenge. Key innovations include a self-tuning VUF mechanism that improves disturbance rejection by 42%, a weighted adaptive optimization strategy that reduces parameter tuning iterations by 37%, and an asymmetric learning factor that balances global exploration and local refinement. Benchmarks using Rastrigin, Griewank, and Sphere functions show superior convergence and 68% stability improvement. Ocean heading simulations of a 7.02 m unmanned surface vehicle (USV) using the Nomoto model show a 91.7% reduction in stabilization time, a 0.9% reduction in overshoot, and a 30% reduction in optimization iterations. The experimental validation under wind and wave disturbances shows that the heading deviation is less than 0.0392°, meeting the IMO MSC.1/Circ.1580 standard, and an 89.5% improvement in energy efficiency. Although the processing time is 12.7% longer compared to the GRO approach, this framework lays a solid foundation for ship autonomy systems, and future enhancements will focus on MPC-based time delay compensation and Field-Programmable Gate Array (FPGA) acceleration. |
| format | Article |
| id | doaj-art-d0e34e3cc8b0409ea2eea021e3c1ab54 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-d0e34e3cc8b0409ea2eea021e3c1ab542025-08-20T01:48:56ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113355810.3390/jmse13030558A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface VehiclesHuixia Zhang0Zhao Zhao1Yuchen Wei2Yitong Liu3Wenyang Wu4School of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaIn this study, a hybrid heading control framework for unmanned surface vehicles (USVs) is proposed, combining variable domain fuzzy Proportional–Integral–Derivative (VUF-PID) with an improved algorithmic Beetle Antennae Search–Particle Swarm Optimization–Simulated Annealing (BAS-PSO-SA) optimization to address the multi-objective control challenge. Key innovations include a self-tuning VUF mechanism that improves disturbance rejection by 42%, a weighted adaptive optimization strategy that reduces parameter tuning iterations by 37%, and an asymmetric learning factor that balances global exploration and local refinement. Benchmarks using Rastrigin, Griewank, and Sphere functions show superior convergence and 68% stability improvement. Ocean heading simulations of a 7.02 m unmanned surface vehicle (USV) using the Nomoto model show a 91.7% reduction in stabilization time, a 0.9% reduction in overshoot, and a 30% reduction in optimization iterations. The experimental validation under wind and wave disturbances shows that the heading deviation is less than 0.0392°, meeting the IMO MSC.1/Circ.1580 standard, and an 89.5% improvement in energy efficiency. Although the processing time is 12.7% longer compared to the GRO approach, this framework lays a solid foundation for ship autonomy systems, and future enhancements will focus on MPC-based time delay compensation and Field-Programmable Gate Array (FPGA) acceleration.https://www.mdpi.com/2077-1312/13/3/558unmanned surface vehicles (USVs)heading controlBAS-PSO-SA optimizationvariable universe fuzzy PIDco-evolutionary algorithmsenergy-efficient steering |
| spellingShingle | Huixia Zhang Zhao Zhao Yuchen Wei Yitong Liu Wenyang Wu A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles Journal of Marine Science and Engineering unmanned surface vehicles (USVs) heading control BAS-PSO-SA optimization variable universe fuzzy PID co-evolutionary algorithms energy-efficient steering |
| title | A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles |
| title_full | A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles |
| title_fullStr | A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles |
| title_full_unstemmed | A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles |
| title_short | A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles |
| title_sort | self tuning variable universe fuzzy pid control framework with hybrid bas pso sa optimization for unmanned surface vehicles |
| topic | unmanned surface vehicles (USVs) heading control BAS-PSO-SA optimization variable universe fuzzy PID co-evolutionary algorithms energy-efficient steering |
| url | https://www.mdpi.com/2077-1312/13/3/558 |
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