School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China.
Shenzhen Institute of Guangdong Ocean University, Shenzhen 518120, China.
Comput Intell Neurosci. 2022 Jul 7;2022:6515773. doi: 10.1155/2022/6515773. eCollection 2022.
In this study, a robust fixed-time H∞ trajectory tracking controller for marine surface vessels (MSVs) is proposed based on self-structuring neural network (SSNN). First, a fixed-time H Lyapunov stability theorem is proposed to guarantee that the MSV closed-loop system is fixed-time stable (FTS) and the gain is less than or equal to . This shows high accuracy and strong robustness to the approximation errors. Second, the SSNN is designed to compensate for the model uncertainties of the MSV system, marine environment disturbances, and lumped disturbances term constituted by the actuator faults (AFs). The SSNN can adjust the network structure in real time through elimination rules and split rules. This reduces the computational burden while ensuring the control performance. It is proven by Lyapunov stability that all signals in the MSV system are stable and bounded within a predetermined time. Finally, theoretical analysis and numerical simulation verify the feasibility and effectiveness of the control scheme.
在这项研究中，提出了一种基于自组织神经网络（SSNN）的稳健固定时间 H∞轨迹跟踪控制器，用于海洋水面船舶（MSV）。首先，提出了一个固定时间 H 李雅普诺夫稳定性定理，以保证 MSV 闭环系统是固定时间稳定（FTS），增益小于或等于 。这表明对逼近误差具有高精度和强鲁棒性。其次，设计 SSNN 来补偿 MSV 系统的模型不确定性、海洋环境干扰以及由执行器故障（AFs）构成的集中干扰项。SSNN 可以通过消除规则和分裂规则实时调整网络结构。这降低了计算负担，同时确保了控制性能。通过李雅普诺夫稳定性证明，MSV 系统中的所有信号都在预定时间内稳定且有界。最后，理论分析和数值模拟验证了控制方案的可行性和有效性。
