Event-triggered self-regulating integrated PID extreme optimization and fault-tolerant strategy for floating offshore platforms driven by TBG-EDF dual finite-time learning mechanism

Floating offshore platforms (FOPs) face the challenge of anchor chain failures due to their unique operating environments. This directly impacts the platform's safety and stability. Traditional control methods are often ineffective in addressing anchor chain failures. Therefore, this study proposes a novel event-triggered, self-tuning integrated PID control strategy based on finite-time learning to improve the stability and fault handling capabilities of FOP systems. This study introduces a preset finite-time convergence function based on a time-based generator (TBG) in the control design, combined with a nonlinear error-driven mechanism to achieve finite-time convergence. By integrating a composite variable construction method with neural network approximation techniques, a state mapping mechanism adaptable to mixed uncertainties is established. Furthermore, this study designs a control system that integrates an event-triggered dynamic mechanism with a finite-time convergence paradigm. Through strategic parameter scheduling, this control strategy achieves coordinated optimal configuration and extreme performance optimization under multiple performance metrics in an offline phase. A rigorous stability analysis of the designed control strategy using Lyapunov stability theory demonstrates its effectiveness in terms of asymptotic stability and finite-time convergence. Simulations are conducted on a real FOP system. Results demonstrate that the control strategy effectively addresses anchor chain failures and internal and external uncertain dynamics.