Analysis and applications of artificial neural networks
Analysis and applications of artificial neural networks
Automatica (Journal of IFAC)
Brief paper: Synchronization of bilateral teleoperators with time delay
Automatica (Journal of IFAC)
Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications
Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications
Synchronization and Control of Multiagent Systems
Synchronization and Control of Multiagent Systems
On adaptive synchronization control of coordinated multirobots with flexible/rigid constraints
IEEE Transactions on Robotics
Position synchronization of multiple motion axes with adaptive coupling control
Automatica (Journal of IFAC)
Decentralized control of cooperative robots without velocity-force measurements
Automatica (Journal of IFAC)
Decentralized adaptive coordinated control of multiple robot arms without using a force sensor
Automatica (Journal of IFAC)
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In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on the leader-follower network communication topology. The justification of introducing two adaptive Radial Basis Function Neural Networks (RBF NN), also called neuro-agents, is to facilitate the whole control system design and analysis. Otherwise such design is impossible with classical analytical procedure. The first agent is the neuro-compensator to accommodate uncertainty associated with the follower manipulators, and the second agent is the neuro-estimator to obtain acceleration of the leader manipulator. Correspondingly the stability analysis of the designed control system is formulated with Lyapunov method. Finally numerical bench tests under various critical conditions are conducted to validate the effectiveness of the proposed approach.