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Design and implementation of self-tuning control method for the underwater spherical robot

机译：水下球形机器人自整定控制方法的设计与实现

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Considering the complicated disturbance in underwater circumstance, usually it is difficult to solve the control problem when the robot changes its motion state or it is subject to ocean currents, its performance deteriorates since the fixed set of parameters is no longer valid for the new conditions. Thus, in this paper, an auto-tune PID (Proportional + Integral + Derivative)-like controller based on Neural Networks is applied to our amphibious spherical underwater robot, which has a great advantage on processing online for the robot due to their nonlinear dynamics. The Neural Networks (NN) plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. The performance of the NN-based controller is investigated in ADAMS and MATLAB cooperative simulation. The velocity of the spherical robot can be controlled to precisely track desired trajectory in body-fixed coordinate system. Additionally, real time experiments on our underwater spherical robot are conducted to show the effectiveness of the algorithm.
机译：考虑到水下环境的复杂干扰，通常当机器人改变其运动状态或受到海流的影响时，很难解决控制问题，因为固定的参数集对于新的条件不再有效，因此其性能下降。因此，本文将基于神经网络的自整定PID（比例+积分+导数）控制器应用于我们的两栖球形水下机器人，由于其非线性动力学，在在线处理方面具有很大的优势。 。神经网络（NN）的作用是自动估计可实现系统稳定性的PID增益的合适集合。 NN在线调整获得较小位置跟踪误差的控制器增益。在ADAMS和MATLAB协同仿真中研究了基于NN的控制器的性能。球形机器人的速度可以控制，以精确跟踪人体固定坐标系中的所需轨迹。此外，我们在水下球形机器人上进行了实时实验，以证明该算法的有效性。

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