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

Abstract

The underwater vehicle-manipulator systems (UVMS) face significant challenges in trajectory tracking and motion planning because of external disturbance (current and payload) and kinematic redundancy. Former algorithms can finish the tracking of end-effector (EE) and free of singularity redundancy solution alone. However, only a few analytical studies have been conducted on coordinated motion planning of UVMS considering the dynamics controller. This article introduces a combined dynamics and kinematics networked fuzzy task priority motion planning method to solve the above problems. It avoids the assumption of perfect dynamic control. Firstly, to eliminate the kinematics error, a dynamic transformation method from joint space to task space is proposed. Without chattering, an outer loop sliding mode controller is designed for tracking EE’s trajectory. Further, to ensure the underwater vehicle’ posture stability and joint constraint, a task priority frame with kinematics error is used to planning the coordinated motion of UVMS, in which the posture and joint limits map into the null space of prioritized tasks, and weight gains are adopted to guarantee orthogonality of secondary tasks. On top of that, the gain weighted are updated by the networked fuzzy logic. The proposed algorithm achieves better coordinated motion planning and tracking performance. Effectiveness is validated by numerical simulation.

A robust optimal control by grey wolf optimizer for underwater vehicle-manipulator system

“…There have been a variety of control methods [2,3] proposed for UVMS by scholars in the field, including proportional-integral-derivative (PID) [4,5], expert system [6,7], fuzzy control theory [8], active disturbance rejection control (ADRC) [9], model predictive control (MPC) [10][11][12], neural network control [13], sliding mode control (SMC) [14,15], etc. However, many of these control theories suffer from limitations, such as ignoring disturbances, losing optimal control performance, reliance on accurate modeling, or high calculation complexity.…”

An Adaptive Control Method with Disturbance Estimation for Underwater Manipulation

“…However, one major drawback of SMC is chattering control action, which may be strengthened by high-order dynamic uncertainties in UVMS motion. To ensure tracking quality against disturbance, the fuzzy logic control (FLC) scheme [18,19] is also employed to handle the highly nonlinear nature of the vehicle-manipulator system by heuristics fuzzy control rules and the appropriate choice of membership functions for the input/output signals. However, FLC requires substantially more computational time due to its complex decision-making process.…”