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A neural adaptive prescribed performance controller for the chaotic PMSM stochastic system

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Abstract

The permanent magnet synchronous motor (PMSM) is a highly nonlinear, multivariable, strongly coupled and complex control system, and is susceptible to stochastic disturbances. Also considering chaotic oscillations, prescribed performance constraint, full-state constraints, input constraints and system stochastic noise, a neural adaptive prescribed performance controller (NAPPC) is presented in this paper. Firstly, a novel unified prescribed performance quartic cosine type barrier Lyapunov function (QC-BLF) is designed to handle both prescribed performance constraint and full-state constraints to ensure that the PMSM has higher safety, faster response time, and lower tracking error simultaneously. This QC-BLF can achieve effective control on asymmetric constrained, symmetric constrained, or unconstrained PMSM without redesigning the controller. In addition, radial basis function neural network (RBFNN) is used to approximate the unknown nonlinearities and unknown gains of the system. A tracking differentiator (TD) is adopted to effectively solve the “explosion of complexity” caused by the backstepping method and an error compensation mechanism is designed to compensate for the filtering error generated by the TD. Based on the above, a NAPPC is implemented. This controller ensures that all closed-loop signals are eventually bounded, all prescribed performance constraint, state constraints and input constraints are achieved, and the PMSM is successfully freed from chaotic oscillations. Finally, the comparative simulation results with the method in other paper verify the effectiveness and superiority of the proposed controller.

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Funding

The authors would like to appreciate all the editors and reviewers for improving the quality of this article. This work was supported by the National Key Research and Development Program of China (2018YFB1304800) and Key Research and Development Program of Guangdong Province (2020B090926002).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Yankui Song, Yaoyao Tuo & Junyang Li

  2. College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Yankui Song, Yaoyao Tuo & Junyang Li

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  1. Yankui Song
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YS, YT and JL. The first draft of the manuscript was written by YT and YS. YS and YT have the same contribution to the article. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Junyang Li.

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Song, Y., Tuo, Y. & Li, J. A neural adaptive prescribed performance controller for the chaotic PMSM stochastic system. Nonlinear Dyn 111, 15055–15073 (2023). https://doi.org/10.1007/s11071-023-08634-0

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