基于神经动力学模型预测的多AUV编队自适应控制方案

A Neural Dynamics Model Prediction-Based Adaptive Control System for AUV Formation Control

  • 摘要: 【目的】提出一种能够解决复杂海流和障碍干扰影响多自主水下航行器(AUV)编队控制问题的方案。【方法】首先,针对各种障碍干扰以及动态避障过程中期望收敛速度过快导致的AUV滞回问题,设计了一种基于神经动力学模型预测的多AUV编队自适应控制方案(NDP-ABS),引入活性源、抑制源,结合最优控制实现了动态避障、编队控制和期望跟踪,解决了势场类算法的局部最优问题。其次,考虑到NDP环节在控制律中引入的未知非线性因子以及海流扰动,基于非线性自适应反步法对AUV期望跟踪控制器进行设计,解决了浅层海流扰动以及非线性因子对AUV期望跟踪控制的干扰。最后,利用Lyapunov理论证明了系统的稳定性。【结果】利用六组对比仿真实验对NDP-ABS方案的抗干扰和避障性能进行了测试,仿真结果验证了其有效性。【结论】NDP-ABS编队方案具有避障代价低、抗海流干扰能力强、稳定性高等优点,在多AUV非显式编队控制中具有明显优势。

     

    Abstract: Objectives Provide a solution for the formation control issue that arises when AUVs are subjected to interference from obstacles and complex ocean currents. Methods To tackle the issue of AUV hysteresis resulting from an overly rapid predicted convergence speed during dynamic obstacle avoidance, a multi AUV formation adaptive control method (NDP-ABS) based on brain dynamics model prediction was created. In order to solve the local optimization problem of potential field methods, active and inhibitory sources were created. When paired with optimal control, dynamic obstacle avoidance, formation control, and predicted tracking were accomplished. Second, a nonlinear adaptive backstepping method is used to design the AUV expected tracking controller, which resolves the interference of shallow ocean current disturbances and nonlinear factors on the AUV expected tracking control. This is done in consideration of the unknown nonlinear factors and ocean current disturbances introduced in the control law of the NDP process. Finally, Lyapunov theory was used to demonstrate the system's stability. Results The anti-interference and obstacle avoidance performance of the NDP-ABS system was tested using six sets of comparative simulation tests, and the simulation results confirmed its efficacy. Conclusions The NDP-ABS formation scheme offers several benefits, including cheap obstacle avoidance costs, robust resistance to interference from ocean currents, high stability, and clear advantages in non-explicit formation control of multiple autonomous underwater vehicles.

     

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