Multi-parameter Identification of Underwater Equipment Joint Motor

With the rapid development of unmanned undersea systems, joint motors play an important role as the core driving devices of underwater robots, underwater manipulators, and other underwater equipment. In this paper, the on-line multi-parameter identification of an underwater joint motor is studied to solve the problem that the precision and stability of motor control are deteriorated due to the change of motor parameters under the influence of different working environments. Specifically, the method of increasing steady-state operating points is used to realize multi-parameter full rank identification. At the same time, to improve the accuracy and robustness of the identification method, this study investigates the feasibility of extended Kalman filter(EKF) and H∞ filter(H-infinity filter, HIF) in the identification of motor parameters. Then a new joint estimation method based on adaptive EKF(AEKF) and adaptive HIF(AHIF) is proposed. Through simulation comparison, it is found that in parameter identification, the steady-state standard deviation of the proposed AEKF+AHIF joint estimation method is reduced by 84.7% compared with that of the AEKF method, and the accuracy is increased by 91.7% compared with that of the AHIF method. The joint estimation method can provide theoretical and technical support for the stable and efficient operation of underwater joint motors.