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<p>In this paper, the optimal predefined-time tracking method of adaptive dynamic programming is investigated for the three-dimensional trajectory tracking control problem of underactuated AUVs. First, a new cost function is designed to reduce the computational complexity by improving the reward mechanism of the actor-critic neural network, and an optimal control strategy with global predefined-time convergence is proposed on the basis of the backstepping technique. Second, a 3D obstacle avoidance scheme is first introduced in trajectory tracking by optimizing the 3D global vector field and integrated with the control system to dynamically regulate the error output via the game theory principle, which makes the strategy more suitable for obstacle avoidance. Third, a variability performance function is proposed not only to improve the control accuracy but also to constrain the errors in the full state; thus, the convergence speed and stability of the system are further enhanced. Finally, the theorem proof and simulation results demonstrate the effectiveness of the proposed method.</p>

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Predefined time optimal control for underactuated AUVs based on 3D cooperative game obstacle avoidance without target points

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Zhijian Feng,
  • Haitao Liu,
  • Xuehong Tian,
  • Qingqun Mai,
  • Jing Zhang

摘要

In this paper, the optimal predefined-time tracking method of adaptive dynamic programming is investigated for the three-dimensional trajectory tracking control problem of underactuated AUVs. First, a new cost function is designed to reduce the computational complexity by improving the reward mechanism of the actor-critic neural network, and an optimal control strategy with global predefined-time convergence is proposed on the basis of the backstepping technique. Second, a 3D obstacle avoidance scheme is first introduced in trajectory tracking by optimizing the 3D global vector field and integrated with the control system to dynamically regulate the error output via the game theory principle, which makes the strategy more suitable for obstacle avoidance. Third, a variability performance function is proposed not only to improve the control accuracy but also to constrain the errors in the full state; thus, the convergence speed and stability of the system are further enhanced. Finally, the theorem proof and simulation results demonstrate the effectiveness of the proposed method.