Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 12, Pages 2173-2188Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2176141
Keywords
Compact form dynamic linearization; data-driven control; model-free adaptive control; multiple-input and multiple-output nonlinear system; partial form dynamic linearization; pseudo-partial derivative; stability
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Funding
- National Natural Science Foundation of China [60834001, 61120106009, 61040050]
- Fundamental Research Funds for the Central Universities [2011JBM201]
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In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.
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