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

In this study, a model-free adaptive predictive control (MFAPC) method is proposed for a class of unknown nonlinear non-affine multiple-input and multiple-output (MIMO) systems based on a novel dynamic linearization technique and a new time-varying Pseudo-Jacobian matrix (PJM) parameter. The advanta...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.102852-102866
Main Authors: Guo, Yuan, Hou, Zhongsheng, Liu, Shida, Jin, Shangtai
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive control
Control stability
Control systems
Control systems design
Data models
data-driven control (DDC)
Discrete time systems
Jacobi matrix method
Jacobian matrix
Mathematical model
MIMO (control systems)
MIMO communication
model-free adaptive control (MFAC)
Model-free adaptive predictive control (MFAPC)
multiple-input and multiple-output (MIMO) nonlinear system
Nonlinear systems
Predictive control
Predictive models
Stability analysis
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Summary:In this study, a model-free adaptive predictive control (MFAPC) method is proposed for a class of unknown nonlinear non-affine multiple-input and multiple-output (MIMO) systems based on a novel dynamic linearization technique and a new time-varying Pseudo-Jacobian matrix (PJM) parameter. The advantages of the proposed method are that it does not need the model information in the control system design, and it can avoid a short-sighted control decision and shows better control performance by integrating the idea of predictive control. The applicability and effectiveness of the proposed control scheme have been verified through rigorous mathematical analysis and extensive simulations.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2931198