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

• Published in: International Conference on Industrial Mechatronics and Automation (Online) pp. 632 - 637
• Main Authors: Yanlin He, Shuxiang Guo, Liwei Shi, Huiming Xing, Zhan Chen, Shuxiang Su
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2017
• Subjects: Artificial neural networks; Auto-tuning; Cooperative Simulation; Mathematical model; Neurons; Neutral Network PID Control; Oceans; Robot kinematics; Underwater Spherical Robot; Unmanned underwater vehicles; Virtual Prototype
• ISBN: 9781509067589; 1509067582
• ISSN: 2152-744X
• DOI: 10.1109/ICMA.2017.8015890

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its motion state or it is subject to ocean currents, its performance deteriorates since the fixed set of parameters is no longer valid for the new conditions.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.