Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-11-30 DOI:10.1109/TNN.2011.2176141
Zhongsheng Hou, Shangtai Jin
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引用次数: 445

Abstract

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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一类MIMO非线性离散系统的数据驱动无模型自适应控制。
针对一类一般多输入多输出非线性离散系统,提出了一种基于动态线性化技术(DLT)的数据驱动无模型自适应控制(MFAC)方法。DLT包括紧凑形式动态线性化、部分形式动态线性化和完整形式动态线性化。该方法的主要特点是控制器设计仅依赖于被控对象的测量输入/输出数据。分析和大量仿真结果表明,MFAC保证了有界输入有界输出的稳定性和跟踪误差的收敛性。
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来源期刊
IEEE transactions on neural networks
IEEE transactions on neural networks 工程技术-工程:电子与电气
自引率
0.00%
发文量
2
审稿时长
8.7 months
期刊最新文献
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