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Event-trigger-based Neural Network Controller for Pure-feedback Nonlinear Systems with Full-state Constraints

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  • Control Theory and Applications
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Abstract

In this paper, we study the event-triggered output tracking control problem for a class of pure-feedback nonlinear systems subject to asymmetric time-varying full-state constraints, lumped disturbances and uncertainties. By introducing a state-dependent function, the original constrained system is transformed into a new system which is completely equivalent to the former. Then, an event-triggered adaptive neural network (NN) controller is developed to stabilize the new system. The problems of circular design and feasibility conditions are circumvented by coordinate transformation technique based on the dynamic surface control (DSC) simultaneously. It has been shown that the output tracking error can converge to an arbitrary predefined compact set under the proposed method. In addition, the boundedness of all signals is ensured through rigorous proof. And meanwhile, it is demonstrated that the designed controller has the ability to maintain the desired state constraints under the lumped disturbances and uncertainties, and reduce the burden of communication transmission. Finally, the simulation results show the effectiveness of the control algorithm.

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Funding

This work was supported in part by the National Natural Science Foundation of China (62073187 and 62073189), the Major Scientific and Technological Innovation Project in Shandong Province (2019JZZY011111).

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Authors and Affiliations

  1. School of Engineering, Qufu Normal University, Rizhao, 276826, China

    Yang Gao, Zhongcai Zhang & Linran Tian

Authors
  1. Yang Gao
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  2. Zhongcai Zhang
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  3. Linran Tian
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Corresponding author

Correspondence to Zhongcai Zhang.

Additional information

Yang Gao received his B.S. degree in mathematics and applied mathematics from Liaocheng University, Liaocheng, China, in 2019. He is currently pursuing an M.S. degree in the School of Engineering, Qufu Normal University. His research interests include the nonlinear systems and event-triggered control.

Zhongcai Zhang received his M.S. degree in automatic engineering from Qufu Normal University, Qufu, China, in 2013 and a Ph.D. degree in automatic control from Southeast University, Nanjing, China, in 2016. He is currently an associate professor with the School of Engineering, Qufu Normal University, Rizhao, China. His current research interests include nonlinear system control, nonholonomic system control, underactuated system control, adaptive theory, and robot applications.

Linran Tian received his B.S. degree in mathematics and applied mathematics from Qufu Normal University, Qufu, China, in 2019, where he is currently working toward an M.S. degree in the School of Engineering. His research interests include the control of nonlinear systems and the control of flexible-joint manipulators.

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Gao, Y., Zhang, Z. & Tian, L. Event-trigger-based Neural Network Controller for Pure-feedback Nonlinear Systems with Full-state Constraints. Int. J. Control Autom. Syst. 20, 1226–1237 (2022). https://doi.org/10.1007/s12555-021-0172-3

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  • DOI: https://doi.org/10.1007/s12555-021-0172-3

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