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Robust NN-based decentralized optimal tracking control for interconnected nonlinear systems via adaptive dynamic programming

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Abstract

This study presents a decentralized robust optimal tracking control scheme for a class of interconnected nonlinear systems with disturbances and actuator attacks. To save the computational energy resources, an adaptive event condition in the sensor channel and an estimated state-driven event condition in the control channel are established. Based on the output triggered information, a novel decentralized proportional-integral observer (PIO) is designed aided by the neighbor information, in which the system states and lumped disturbances are estimated. By utilizing the observer information, a reliable decentralized controller is designed. In the proposed control scheme, two control parts are included. An event-based discontinuous part is designed to force the tracking error to reach the sliding mode manifold, and an optimal control part is presented to release the effects of disturbance and minimize the control cost, the estimated disturbance values are also integrated into the control scheme as a compensation part. In addition, the optimal control problem is solved based on the neural network (NN) and adaptive dynamic programming (ADP), the stability and the weight vector of the tracking system are demonstrated, and finally, the application to the interconnected nonlinear power system verifies the merit of the presented control approach.

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Acknowledgements

This work was supported by the Scientific and Technical Programs of Sichuan Province of China (under Grant 2021YJ0092) and the National Key R&D Program of China (under Grant 2018YFB1307401).

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Authors and Affiliations

  1. College of Electrical Engineering, Sichuan University, Chengdu, 610065, China

    Bin Guo, Songyi Dian & Tao Zhao

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  1. Bin Guo
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  2. Songyi Dian
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  3. Tao Zhao
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Correspondence to Songyi Dian.

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Guo, B., Dian, S. & Zhao, T. Robust NN-based decentralized optimal tracking control for interconnected nonlinear systems via adaptive dynamic programming. Nonlinear Dyn 110, 3429–3446 (2022). https://doi.org/10.1007/s11071-022-07771-2

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