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Sampled-Data Model-Free Adaptive Control for Nonlinear Continuous-Time Systems
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DOI：10.1109/TCYB.2023.3324060.png)
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Abstract
En 中文
This work aims at presenting a new sampled-data model-free adaptive control (SDMFAC) for continuous-time systems with the explicit use of sampling period and past input and output (I/O) data to enhance control performance. A sampled-data-based dynamical linearization model (SDDLM) is established to address the unknown nonlinearities and nonaffine structure of the continuous-time system, which all the complex uncertainties are compressed into a parameter gradient vector that is further estimated by designing a parameter updating law. By virtue of the SDDLM, we propose a new SDMFAC that not only can use both additional control information and sampling period information to improve control performance but also can restrain uncertainties by including a parameter adaptation mechanism. The proposed SDMFAC is data-driven and thus overcomes the problems caused by model-dependence as in the traditional control design methods. The simulation study is performed to demonstrate the validity of the results.
Keywords:
Uncertainty
Adaptation models
Observers
Adaptive control
Robustness
Nonlinear dynamical systems
Mathematical models
Continuous-time systems
model-free adaptive control (MFAC)
nonlinear nonaffine systems
sampled-data control
sampled-data-based dynamical linearization model (SDDLM)
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W