Abstract
Precise motion control of remotely operated vehicles plays an important role in a great number of submarine missions. However, the high-performance operations are difficult to realize due to the uncertainty in system modeling with self-disturbance. On the basis of the multibody system dynamics, self-disturbances from the tether and manipulator have been systematically analyzed in order to transform them into observed forces. A novel S surface–based adaptive recurrent wavelet neural network control system has been proposed on the nonlinear control of underwater vehicles, with its recurrent wavelet neural network structure designed for the approximation of the uncertain dynamics. Moreover, a robust function has been proposed to improve system robustness and convergence. The comparison shows that the remotely operated vehicle operation performance including the three-dimensional path following and vehicle-manipulator coordinate control has been greatly improved.
Highlights
The motions of Remotely operated vehicle manipulator system (ROVMS) are usually teleoperated from a human pilot with a master manipulator on the surface control platform, the Remotely operated vehicles (ROVs) is applied as a mobile platform for its slave manipulator operation
The teleoperated master–slave manipulation systems often puzzled with simultaneously control on tens of degree of freedoms (DOFs) because of the system interactions between the vehicle and manipulators
In the last decades, increasing research has been focused on precise motion control of underwater vehicle and manipulator systems
Summary
Operated vehicles (ROVs) have been applied worldwide for various operations such as environmental survey, harbor inspection, petroleum hunting, and scientific investigation and even wrecks recovering. Remotely operated vehicle manipulator system (ROVMS) plays an important role in a number of submarine missions for marine science, oil and gas exploration, and salvage. In these application fields, the motions of ROVMS are usually teleoperated from a human pilot with a master manipulator on the surface control platform, the ROV is applied as a mobile platform for its slave manipulator operation. the teleoperated master–slave manipulation systems often puzzled with simultaneously control on tens of degree of freedoms (DOFs) because of the system interactions between the vehicle and manipulators. In the last decades, increasing research has been focused on precise motion control of underwater vehicle and manipulator systems. some projects endeavor more autonomous operation on the intervention autonomous underwater vehicle (I-AUV), ROVMS with long duration of operation time, and. The vehicle is fully actuated, observation class ROVMS carries a series of difficulties in precise motion control and operation due to current disturbances, coupled nonlinearities from the tether and manipulator.. This study has constructed dynamic models for the ROVMS on the basis of a multibody system and systematically analyzed the operation disturbances from tether and manipulator. 2. A novel S surface–based adaptive recurrent wavelet neural network (SARWNN) controller system has been proposed to approximate dynamic uncertainty and realize nonlinear control for the ROVMS. The function of EKF is to estimate covariance matrix of the current position state, on the basis of ROV kinematic models. Since the vehicle velocity and posture information obtained from DVL and magnetic compass could involve sensor noise, the EKF-based dead reckoning block can obtain relatively accurate position output through measurement update and state estimation. Through Barbalat’s Lemma,36ÀST KsS is bounded, the trajectory error will converge into zero as the time converges to infinity
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