Abstract
This paper proposes a radial basis function neural network adaptive backstepping controller (RBFNN-ABC) for multiple-input multiple-output (MIMO) nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated by the backstepping design. This problem may create an unpredictable and unfavorable influence on control performance because higher-order derivative term errors are introduced into the neural approximation model. Finally, simulation results demonstrate that the output tracking error between the plant output and the desired reference can be made arbitrarily small.
Original language | English |
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Title of host publication | Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 |
Pages | 4946-4951 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2009 |
Event | 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 - San Antonio, TX, United States Duration: 2009 Oct 11 → 2009 Oct 14 |
Other
Other | 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 |
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Country | United States |
City | San Antonio, TX |
Period | 2009/10/11 → 2009/10/14 |
Keywords
- Adaptive
- Backstepping
- MIMO nonlinear systems
- Radial basis function (RBF) neural networks (NNs)
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction