Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 7, Pages 1162-1167Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2146788
Keywords
Adaptive control; neural networks; nonlinear multi-input-multi-output discrete-time systems; output feedback control
Categories
Funding
- National Natural Science Fund of China [61074014, 60874056]
- National Fundamental Research 973 Program of China [2011CB302801]
- Macau Science and Technology Development Fund [008/2010/A1]
- Educational Department Fund of Liaoning Province [L2010181]
- Natural Science Fund of Liaoning Province [20102095]
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This brief studies an adaptive neural output feedback tracking control of uncertain nonlinear multi-input-multi-output (MIMO) systems in the discrete-time form. The considered MIMO systems are composed of n subsystems with the couplings of inputs and states among subsystems. In order to solve the noncausal problem and decouple the couplings, it needs to transform the systems into a predictor form. The higher order neural networks are utilized to approximate the desired controllers. By using Lyapunov analysis, it is proven that all the signals in the closed-loop system is the semi-globally uniformly ultimately bounded and the output errors converge to a compact set. In contrast to the existing results, the advantage of the scheme is that the number of the adjustable parameters is highly reduced. The effectiveness of the scheme is verified by a simulation example.
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