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An ABC-Optimized Multimodal Intelligent Adaptive Control Strategy for Six DOF Wave Compensation
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DOI：10.1109/tfuzz.2026.3695951.png)
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Abstract
En 中文
Ship-borne Stewart platforms ensure safe marine transportation by compensating for wave-induced six DOF motions, but face challenges: 1) strong nonlinear wave interference introduces significant time-varying uncertainty; 2) abrupt load perturbations cause parameter fluctuations, degrading tracking precision; and 3) complex interference environments reduce control robustness. To address these, an artificial bee colony (ABC) algorithm-optimized fuzzy radial basis function neural network PI control strategy is proposed. This approach integrates ABC optimization, fuzzy logic, and RBF neural networks, overcoming empirical parameter-tuning limitations and single-controller flaws. The controller exploits fuzzy inference to provide fast response and robust adaptation without relying on an accurate model; to overcome the inherent limitations of rule-based regulation and further improve compensation accuracy under complex nonlinear, an RBF network is integrated to perform data-driven nonlinear refinement, enabling multimodal collaboration and intelligent adaptability. The results under Sea States 3–-5 with superimposed random measurement noise and abrupt load perturbations indicate that, compared with conventional PI, fuzzy PI, and active disturbance rejection control, the proposed fuzzy-inference-driven and neural-refined multimodal controller achieves consistently higher compensation accuracy and stability, and exhibits strong robustness against wave disturbances, load variations, and measurement noise, offering a reliable solution for high-precision motion control in harsh marine environments.
Keywords:
Artificial bee colony algorithm-optimized fuzzy radial basis function neural network PI control strategy (ABC-FNN-PI)
permanent-magnet synchronous motor (PMSM)
ship-borne Stewart platform
wave compensation
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