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ZNN-Based Gait Optimization for Humanoid Robots with ALIP and Inequality Constraints

Author

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  • Yuanji Liu

    (School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
    Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Hao Jiang

    (Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Haiming Mou

    (Shanghai Droid Robot Co., Ltd., Shanghai 200093, China)

  • Qingdu Li

    (School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
    Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Jianwei Zhang

    (Department of Informatics, University of Hamburg, 20146 Hamburg, Germany)

Abstract

This paper presents a zeroing neural networks (ZNN)-based gait optimization strategy for humanoid robots. First, the algorithm converts the angular momentum linear inverted pendulum (ALIP)-based gait planning problem into a time-varying quadratic programming (TVQP) problem by adding adaptive adjustment factors and physical limits as inequality constraints to avoid system oscillations or instability caused by large fluctuations in the robot’s angular momentum. Secondly, This paper proposes a real-time and efficient solution for TVQP based on an integral strong predefined time activation function zeroing neural networks (ISPTAF-ZNN). Unlike existing ZNN approaches, the proposed ISPTAF-ZNN is enhanced to achieve convergence within a strong predefined-time while exhibiting noise tolerance. This ensures the desired rapid convergence and resilience for applications requiring strict time efficiency. The theoretical analysis is conducted using Lyapunov stability theory. Finally, the comparative experiments verify the convergence, robustness, and real-time performance of the ISPTAF-ZNN in comparison with existing ZNN approaches. Moreover, comparative gait planning experiments are conducted on the self-built humanoid robot X02. The results demonstrate that, compared to the absence of an optimization strategy, the proposed algorithm can effectively prevent overshoot and approximate energy-efficient responses caused by large variations in angular momentum.

Suggested Citation

  • Yuanji Liu & Hao Jiang & Haiming Mou & Qingdu Li & Jianwei Zhang, 2025. "ZNN-Based Gait Optimization for Humanoid Robots with ALIP and Inequality Constraints," Mathematics, MDPI, vol. 13(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:954-:d:1611723
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    References listed on IDEAS

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    1. Wen, Ue-Pyng & Lan, Kuen-Ming & Shih, Hsu-Shih, 2009. "A review of Hopfield neural networks for solving mathematical programming problems," European Journal of Operational Research, Elsevier, vol. 198(3), pages 675-687, November.
    2. Jin, Jie & Chen, Weijie & Qiu, Lixin & Zhu, Jingcan & Liu, Haiyan, 2023. "A noise tolerant parameter-variable zeroing neural network and its applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 482-498.
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