Wang Rui, Lu Jingwei, Lyu Shuke, Liu Yongtao, Cui Yuchen
School of Emergency Equipment, North China Institute of Science and Technology, Beijing 101601, China.
School of Information and Control Engineering, North China Institute of Science and Technology, Beijing 101601, China.
Sensors (Basel). 2025 May 13;25(10):3088. doi: 10.3390/s25103088.
Traditional guidance and control systems often treat guidance and control systems separately, leading to reduced interception accuracy and responsiveness, especially during high-speed terminal trajectories. These limitations are further exacerbated in autonomous underwater vehicles (AUVs) due to unknown wave/current disturbances, harsh underwater acoustic conditions, and limited sensor capabilities. To address these challenges, this paper studies an integrated guidance and control (IGC) design for AUVs intercepting maneuvering targets with unknown disturbances and unmeasurable system states. The IGC model is derived based on the relative motion equations between the AUV and the target, incorporating the lateral dynamics of the AUV. A model transformation is introduced to synthesize external disturbances with unmeasurable states, extending the resultant disturbance to a new system state. A finite-time convergent extended state observer (ESO) is thus designed for the transformed system to estimate the unknown signals. Using these estimates from the observer, a finite-time event-triggered sliding mode controller is developed, ensuring finite-time convergence of system errors to an adjustable residual set, as rigorously proven through Lyapunov stability analysis. Simulation results demonstrate the superiority of the proposed method in achieving higher interception accuracy and faster response compared to traditional guidance and control approaches with unknown disturbances and unmeasurable states.
传统的制导与控制系统通常将制导系统和控制系统分开处理，导致拦截精度和响应能力降低，尤其是在高速末段轨迹期间。由于未知的波浪/水流干扰、恶劣的水下声学条件以及有限的传感器能力，这些局限性在自主水下航行器（AUV）中进一步加剧。为应对这些挑战，本文研究了一种用于AUV拦截具有未知干扰和不可测量系统状态的机动目标的集成制导与控制（IGC）设计。IGC模型基于AUV与目标之间的相对运动方程推导得出，并纳入了AUV的横向动力学。引入了一种模型变换，以将外部干扰与不可测量状态进行综合，将合成干扰扩展为一个新的系统状态。因此，为变换后的系统设计了一个有限时间收敛的扩展状态观测器（ESO），用于估计未知信号。利用观测器的这些估计值，开发了一种有限时间事件触发滑模控制器，通过李雅普诺夫稳定性分析严格证明，确保系统误差在有限时间内收敛到一个可调的残差集。仿真结果表明，与具有未知干扰和不可测量状态的传统制导与控制方法相比，所提方法在实现更高拦截精度和更快响应方面具有优越性。
