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SCIENCE CHINA Information Sciences, Volume 61, Issue 11: 112201(2018) https://doi.org/10.1007/s11432-017-9363-y

Cooperative deterministic learning control for a group of homogeneous nonlinear uncertain robot manipulators

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  • ReceivedOct 28, 2017
  • AcceptedJan 31, 2018
  • PublishedMay 23, 2018

Abstract

This paper addresses the learning control problem for a group of robotmanipulators with homogeneous nonlinear uncertaindynamics, where all the robots have an identical system structure but the reference signals to be tracked differ. The control objective is twofold: to track on reference trajectories andto learn/identify uncertain dynamics. For this purpose, deterministic learning theoryis combined with consensus theory to find a common neural network(NN) approximation of the nonlinear uncertain dynamics for a multi-robotsystem. Specifically, we first present a control scheme called cooperativedeterministic learning using adaptive NNs to enable the robotic agentsto track their respective reference trajectories on one hand andto exchange their estimated NN weights online through networked communicationon the other. As a result, a consensus about one common NN approximationfor the nonlinear uncertain dynamics is achieved for all the agents.Thus, the trained distributed NNs have a better generalization capabilitythan those obtained by existing techniques. By virtue of the convergenceof partial NN weights to their ideal values under the proposed scheme,the cooperatively learned knowledge can be stored/represented by NNswith constant/converged weights, so that it can be used to improvethe tracking control performance without re-adaptation. Numericalsimulations of a team of two-degree-of-freedom robot manipulatorswere conducted to demonstrate the effectiveness of the proposedapproach.


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