Standoff tracking control of underwater glider to moving target
Standoff tracking control of underwater glider to moving target
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DOI：
10.1016/j.apm.2021.09.011
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发表时间：
2022-02
影响因子：
5
通讯作者：
Wenchuan Zang;Peng Yao;Dalei Song
中科院分区：
文献类型：
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作者：
Wenchuan Zang;Peng Yao;Dalei Song
水下滑翔机对运动目标的距离跟踪控制在海洋观测中具有不可忽视的潜力。为了实现这一目标，本研究提出了一种集成的距离跟踪方法，重点处理滑翔机的耦合运动和欠驱动特性，并抵消由于缺乏导航系统而造成的困境。该方法包括制导算法、基本控制器和强化学习优化器，以提高跟踪精度。利用已验证的OUC-III型滑翔机仿真模型对算法进行了开发，并对跟踪效果进行了评估。自适应导航律Lyapunov制导矢量场法在该框架中生成期望的航向。导航只需要通过航位推算获得的定位信息。针对比例积分微分控制器控制的滑翔机状态不能立即达到要求状态的不足，引入了强化学习优化器。滑动状态构成优化器输入，同时输出补偿旋转信号来驱动可动质量。优化器的奖励函数是为了使滑翔机的位置误差相对于定距圆保持在1 m以内。在此基础上，目标设定为最大化奖励，从而保证最佳准确性。在我们提出的框架中采用了三种强化学习算法并进行了测试。在训练阶段，比较了复合方法和纯强化学习算法的训练速度。为了测试系统的稳定性，我们在验证仿真中设计了不同类型的运动目标、对峙半径和干扰，这些都是训练中没有出现的。该研究可为水下滑翔机的距离跟踪应用提供模式和解决方案。同时，它在现有硬件的基础上扩大了滑翔机的部署范围。
The standoff tracking control of underwater glider to moving targets has non-negligible potentials in oceanic observations. To perform such operations, this study presents an integrated standoff tracking method that focuses on dealing with the coupled motions and under-actuated characteristics of the gliders as well as offsetting the dilemma caused by lacking of navigation system. The proposed approach contains guidance algorithm, basic controller, and reinforcement learning optimizer to improve the tracking accuracy. The verified simulation model for the OUC-III glider is utilized to develop the algorithm and evaluate the effectiveness of tracking. The adaptive navigation law called Lyapunov guidance vector field method generates the desired heading in this framework. The guidance only requires positioning information that is accessible through dead-reckoning. For the deficiency that the glider’s state controlled by the proportional integral differential controller can not immediately reach the required state, the reinforcement learning optimizer is introduced. The gliding status constitutes optimizer input while the compensating rotation signals are outputted to actuate the movable mass. The reward function of the optimizer is designed for maintaining the glider position error within 1 m with respect to the standoff circle. On this basis, the objective is set to maximize the rewards and consequently to guarantee the best accuracy. Three reinforcement learning algorithms are adopted and tested in our proposed framework. In the training phase, the training speeds of composite methods and pure reinforcement learning algorithms are compared. To test the stability, we design different types of moving targets, standoff radium and disturbance in the validation simulation, which have not appeared in the training. This study can provide patterns and solutions for the standoff tracking applications of underwater gliders. Also, it enlarges the glider’s scope of deployment based on the existing hardware.