Data-Driven Finite-Horizon H∞ Tracking Control With Event-Triggered Mechanism for the Continuous-Time Nonlinear Systems

In this article, the neural network (NN)-based adaptive dynamic programming (ADP) event-triggered control method is presented to obtain the near-optimal control policy for the model-free finite-horizon <inline-formula> <tex-math notation="LaTeX">H_\infty </tex-math></i...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 8; pp. 4687 - 4701
Main Authors: Zhang, Huaguang, Ming, Zhongyang, Yan, Yuqing, Wang, Wei
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Adaptive control
Adaptive dynamic programming (ADP)
Artificial neural networks
Closed loops
Control methods
Cost function
data driven
Dynamic programming
event triggered
Feedback control
finite horizon
H-infinity control
Mathematical models
Neural networks
neural networks (NNs)
Nonlinear systems
Optimal control
Power system dynamics
Terminal constraints
Time dependence
Tracking control
Tracking errors
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:In this article, the neural network (NN)-based adaptive dynamic programming (ADP) event-triggered control method is presented to obtain the near-optimal control policy for the model-free finite-horizon <inline-formula> <tex-math notation="LaTeX">H_\infty </tex-math></inline-formula> optimal tracking control problem with constrained control input. First, using available input-output data, a data-driven model is established by a recurrent NN (RNN) to reconstruct the unknown system. Then, an augmented system with event-triggered mechanism is obtained by a tracking error system and a command generator. We present a novel event-triggering condition without Zeno behavior. On this basis, the relationship between event-triggered Hamilton-Jacobi-Isaacs (HJI) equation and time-triggered HJI equation is given in <xref ref-type="theorem" rid="theorem3">Theorem 3 . Since the solution of the HJI equation is time-dependent for the augmented system, the time-dependent activation functions of NNs are considered. Moreover, an extra error is incorporated to satisfy the terminal constraints of cost function. This adaptive control pattern finds, in real time, approximations of the optimal value while also ensuring the uniform ultimate boundedness of the closed-loop system. Finally, the effectiveness of the proposed near-optimal control pattern is verified by two simulation examples.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3116464