Trajectory generation and sliding-mode controller design of an underwater vehicle-manipulator system with redundancy

Abstract

An Underwater Vehicle-Manipulator System (UVMS) can be applied to pick up and carry objects for autonomous manipulation in the water. However, it is difficult to control the motion of the whole system because the movement of a manipulator affects the motion of the vehicle and vice versa. Additionally, a lack of information about the object, such as the shape and inertia, makes motion control even more difficult. In the current paper, a motion control algorithm of the UVMS with redundancy was developed to guarantee the stability robustness when the mass information of the objects is not available. In order to generate the joint trajectories of the manipulator, a redundancy resolution was performed to minimize the restoring moments acting on the vehicle. This means the propulsion energy for controlling the vehicle's motion can be reduced. To control the motion of the system with an unknown parameter, a controller based on the sliding mode theory has been designed. Finally, the effectiveness of the proposed method was verified through a series of simulation for a 3DOF vehicle-3DOF manipulator system.

The Application of PSO‐AFSA Method in Parameter Optimization for Underactuated Autonomous Underwater Vehicle Control

“…The transformation between the inertial frame and the body frame complies with [17] Mathematical Problems in Engineering …”

Chattering-suppression sliding mode control of an autonomous underwater vehicle based on nonlinear disturbance observer and power function reaching law

“…Recently a large variety of controllers have been proposed to deal with the trajectory tracking problem of AUVs, which include PID control (Alvarado et al, 2016; Gupta and Gupta, 2016; Herman, 2009; Sarhadi et al, 2016), fuzzy control (Chen et al, 2016; Londhe et al, 2017; Tseng et al, 2013), adaptive control (Antonelli et al, 2004; Hassanein et al, 2016; Sarkar et al, 1999), robust control (Ismail and Dunnigan, 2013; Santhakumar and Kim, 2014), sliding mode control (Ismail and Putranti, 2015; Kim et al, 2015; Xu et al, 2005) and neural network control (Luo et al, 2014; Taira et al, 2012). Among these controllers, sliding mode control (SMC) has the advantages of easy implementation, fast response, small perturbation of model parameters and high robustness, so it is quite suitable for trajectory tracking control of AUVs.…”

Combined dynamics and kinematics networked fuzzy task priority motion planning for underwater vehicle-manipulator systems

“…Solving the lumped uncertainty for the controller of UVMS has attracted many scholars to study. [21][22][23][24][25][26] In a complex environment, the variety of disturbances include (a) ocean current, (b) payload changes, (c) lift or resistance, (d) noise (noise caused by the sensor and actuator error), (e) time delay or joint lag, and (f) friction and other disturbances. Simultaneously, uncertainty generally includes model parameter uncertain item (mainly generated by model inaccuracy) and parameter uncertain item (often not directly reflected as model parameters).…”