Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 61, Issue 3, Pages 1411-1423Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2013.2262747
Keywords
Energy-based controller (EC); energy evaluation function; friction model; RBF neural network
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Funding
- National Natural Science Foundation of China [61020106003]
- 111 Project [B08015]
- National Key Technology Support Program Project [2012BAF19G00]
- Doctoral Start-up Fund of Liaoning Province of China [20121011]
- Fundamental Research Funds for the Central Universities of China [N110308001]
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This paper proposes the energy-based controller (EC) incorporated with radical basis function (RBF) neural-network compensation (ECRBFC), which is used to swing up the Pendubot and raise it to its uppermost unstable equilibrium position. First, for the known dynamics model of the two-link arm, the EC is designed. In the EC, the singularity is successfully avoided by constructing an appropriate energy evaluation function. Second, as for the friction of the Pendubot, because of the time-varying characteristics, an accurate friction dynamics model cannot be known absolutely; thus, the RBF neural network is introduced to offset the bad effect of friction. Finally, in order to evaluate the performance of ECRBFC, the numerical simulations and the experimental results are given, and by comparing the results with that of other algorithms, it is found that ECRBFC proposed in this paper has better performance under the same conditions.
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