Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems

In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-ou...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2017-08, Vol.28 (8), p.1914-1928
Main Authors: Hou, Zhongsheng, Liu, Shida, Tian, Taotao
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive control
Computer simulation
Control stability
Controllers
Data models
Data-driven control
Discrete time systems
Dynamical systems
lazy learning (LL)
Linearization
Machine learning
Mathematical models
model-free adaptive predictive control (MFAPC)
Nonlinear dynamical systems
Nonlinear dynamics
Nonlinear systems
partial form dynamic linearization (PFDL)
Predictive control
Predictive models
Process parameters
Real time
Robustness (mathematics)
Stability analysis
three-tank water level system
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Summary:In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2016.2561702