Adaptive neural network H∞$H_∞$ control for offshore platform with input delay and nonlinearity - Zhang, Yun; Ma, Hui; Wang, Shu‐Qing; Xu, Jianliang; Su, Hao; Zhang, Jing (IET control theory & applications, 2024)
In this work, an adaptive learning robust controller is proposed to suppress the vibration of offshore platforms, which are subject to waves, winds, varying control delays and parametric perturbations. To realize nonlinear uncertainty approximation under the bounded H∞$H_∞$ performance, the H∞$H_∞$ controller incorporates both an online adaptive part and an offline fixed part. The adaptive part constructed by neural networks adjusts online, while the fixed part is obtained by regulating the H∞$H_∞$ performance. Importantly, adaptive updating strategy does not require accurate values or upper bounds for real‐time control delay or uncertainty. Several comparable experiments demonstrate the feasibility and effectiveness in vibration‐suppression of the designed adaptive controller in shallow/deep water. This scheme significantly reduces system response variations due to structural and hydrodynamic uncertainty, as well as additional random environmental forces caused by winds. To realize nonlinear uncertainty approximation during vibration‐attenuation under the bounded H∞$H_∞$ performance, the proposed H∞$H_∞$ controller incorporates with the online adaptive part and the offline fixed part, respectively. The adaptive part is self‐adjusting based on neural network, and the fixed part is derived through minimizing the generalized H∞$H_∞$ disturbance‐rejection index.