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Event-triggered design for discrete-time nonlinear systems with control constraints

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Abstract

In order to solve the constrained-input problem and reduce the computing resources, a novel event-triggered optimal control method is proposed for a class of discrete-time nonlinear systems. In the proposed method, the event-triggered control policy is applied to the globalized dual heuristic dynamic programming (GDHP) algorithm. Compared with the traditional adaptive dynamic programming (ADP) control, the event-triggered GDHP control can reduce the computation while ensuring the system performance. In this paper, a non-quadratic function is given to code the control constraints and the trigger condition with the stability analysis is provided. Neural networks (NN) are constructed in the GDHP structure, where the model network is designed to identify the unknown nonlinear system, the critic network is used to learn the cost function and its partial derivative, and the action network is designed to obtain the approximate optimal control law. Three simulation examples are presented to demonstrate the performance of the proposed event-triggered design for constrained discrete-time nonlinear systems.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62022061, 61773284, and in part by Tianjin Natural Science Foundation under Grant 20JCYBJC00880.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Chaoxu Mu, Kaiju Liao & Ke Wang

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  1. Chaoxu Mu
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  2. Kaiju Liao
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  3. Ke Wang
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Correspondence to Chaoxu Mu.

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Mu, C., Liao, K. & Wang, K. Event-triggered design for discrete-time nonlinear systems with control constraints. Nonlinear Dyn 103, 2645–2657 (2021). https://doi.org/10.1007/s11071-021-06218-4

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