Journal
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Volume 27, Issue 6, Pages 2680-2687Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2018.2872471
Keywords
End effectors; Recurrent neural networks; Task analysis; Kinematics; Motion planning; quadratic programing (QP); recurrent neural networks (RNNs); redundant robot manipulators
Funding
- National Natural Science Foundation [61603142, 61633010]
- Guangdong Foundation for Distinguished Young Scholars [2017A030306009]
- Guangdong Youth Talent Support Program of Scientific and Technological Innovation [2017TQ04X475]
- Science and Technology Program of Guangzhou [201707010225]
- Fundamental Research Funds for Central Universities [x2zdD2182410]
- Scientific Research Starting Foundation of the South China University of Technology
- National Key R&D Program of China [2017YFB1002505]
- National Key Basic Research Program of China (973 Program) [2015CB351703]
- Guangdong Natural Science Foundation [2014A030312005]
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A novel varying-parameter recurrent neural network [called varying-parameter convergent-differential neural network (VP-CDNN)] is proposed and investigated to solve time-varying convex quadratic programing (QP) problems and applied to solve nonrepetitive problems of redundant robot manipulators in this brief. First, the nonrepetitive problems of redundant robot manipulators are reformulated as a QP scheme. Second, the QP scheme is reformulated as a matrix equation. Third, the proposed VP-CDNN is applied to solve the matrix equation as well as the original QP problem. To illustrate the advantages of VP-CDNN solver, comparison simulations between the VP-CDNN and the fixed-parameter convergent-differential neural network (FP-CDNN) are constructed based on a six-degrees-of-freedom robot manipulator. Two end-effector tasks employed by the VP-CDNN with linear activation function and sinh activation function verify the effectiveness and advantages of the proposed VP-CDNN and its better expansibility. The results of computer simulations and physical experiments demonstrate that the VP-CDNN solver is more effective and accurate than the FP-CDNN solver to solve nonrepetitive problems of redundant robot manipulators.
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