4.1 Article

Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems

Journal

IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 12, Pages 2173-2188

Publisher

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

Funding

  1. National Natural Science Foundation of China [60834001, 61120106009, 61040050]
  2. Fundamental Research Funds for the Central Universities [2011JBM201]

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.1
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available