Switched time delay control based on neural network for fault detection and compensation in robot

Abstract

Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

“…Achieving coordinated teamwork among robots with real-time task distribution necessitates a decentralized framework [31] that covers resilient robot awareness, low-level motion control, and high-level task scheduling [32], [33]. Effective location management in multi-robot networks is critical for collaboration, where decision-making and communication play pivotal roles in mission distribution, presenting substantial challenges in data exchange among robots and the operating station [34].…”