Xu Zhiqiang, Li Wanli, Wang Yanran
School of Mechanical Engineering, Tongji University, Shanghai, China.
Front Neurorobot. 2019 Apr 4;13:11. doi: 10.3389/fnbot.2019.00011. eCollection 2019.
The shipborne manipulator plays an important role in autonomous collaboration between marine vehicles. In real applications, a conventional proportional-derivative (PD) controller is not suitable for the shipborne manipulator to conduct safe and accurate operations under ocean conditions, due to its bad tracing performance. This paper presents a real-time and adaptive control approach for the shipborne manipulator to achieve position control. This novel control approach consists of a conventional PD controller and fuzzy neural network (FNN), which work in parallel to realize PD+FNN control. Qualitative and quantitative tests of simulations and real experiments show that the proposed PD+FNN controller achieves better performance in comparison with the conventional PD controller, in the presence of uncertainty and disturbance. The presented PD+FNN eliminates the requirements for precise tuning of the conventional PD controller under different ocean conditions, as well as an accurate dynamics model of the shipborne manipulator. In addition, it effectively implements a sliding mode control (SMC) theory-based learning algorithm, for fast and robust control, which does not require matrix inversions or partial derivatives. Furthermore, simulation and experimental results show that the angle compensation deviation of the shipborne manipulator can be improved in the range of ±1°.
舰载机械手在海洋运载工具之间的自主协作中发挥着重要作用。在实际应用中，传统的比例 - 微分（PD）控制器由于其跟踪性能较差，不适用于舰载机械手在海洋条件下进行安全准确的操作。本文提出了一种用于舰载机械手实现位置控制的实时自适应控制方法。这种新颖的控制方法由一个传统的PD控制器和模糊神经网络（FNN）组成，它们并行工作以实现PD + FNN控制。仿真和实际实验的定性和定量测试表明，在存在不确定性和干扰的情况下，所提出的PD + FNN控制器与传统PD控制器相比具有更好的性能。所提出的PD + FNN消除了在不同海洋条件下对传统PD控制器进行精确调整的要求，以及对舰载机械手精确动力学模型的要求。此外，它有效地实现了基于滑模控制（SMC）理论的学习算法，用于快速且鲁棒的控制，该算法不需要矩阵求逆或偏导数。此外，仿真和实验结果表明，舰载机械手的角度补偿偏差可在±1°范围内得到改善。
