TY - GEN
T1 - Design and implementation of self-tuning control method for the underwater spherical robot
AU - He, Yanlin
AU - Guo, Shuxiang
AU - Shi, Liwei
AU - Xing, Huiming
AU - Chen, Zhan
AU - Su, Shuxiang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/23
Y1 - 2017/8/23
N2 - 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its motion state or it is subject to ocean currents, its performance deteriorates since the fixed set of parameters is no longer valid for the new conditions.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.
AB - 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its motion state or it is subject to ocean currents, its performance deteriorates since the fixed set of parameters is no longer valid for the new conditions.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.
KW - Auto-tuning
KW - Cooperative Simulation
KW - Neutral Network PID Control
KW - Underwater Spherical Robot
KW - Virtual Prototype
UR - https://www.scopus.com/pages/publications/85030325925
U2 - 10.1109/ICMA.2017.8015890
DO - 10.1109/ICMA.2017.8015890
M3 - Conference contribution
AN - SCOPUS:85030325925
T3 - 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017
SP - 632
EP - 637
BT - 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Mechatronics and Automation, ICMA 2017
Y2 - 6 August 2017 through 9 August 2017
ER -