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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(3): 702-710
Dan Wang and Xu Chen, "H∞-Based Selective Inversion of Nonminimum-phase Systems for Feedback Controls," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 702-710, May 2020. doi: 10.1109/JAS.2020.1003138
Citation: Dan Wang and Xu Chen, "H∞-Based Selective Inversion of Nonminimum-phase Systems for Feedback Controls," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 702-710, May 2020. doi: 10.1109/JAS.2020.1003138
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H∞-Based Selective Inversion of Nonminimum-phase Systems for Feedback Controls

doi: 10.1109/JAS.2020.1003138
Funds:  This work was supported in part by the National Science Foundation (1953155)
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    Abstract
  • Stably inverting a dynamic system model is fundamental to subsequent servo designs. Current inversion techniques have provided effective model matching for feedforward controls. However, when the inverse models are to be implemented in feedback systems, additional considerations are demanded for assuring causality, robustness, and stability under closed-loop constraints. To bridge the gap between accurate model approximations and robust feedback performances, this paper provides a new treatment of unstable zeros in inverse design. We provide first an intuitive pole-zero-map-based inverse tuning to verify the basic principle of the unstable-zero treatment. From there, for general nonminimum-phase and unstable systems, we propose an optimal inversion algorithm that can attain model accuracy at the frequency regions of interest while constraining noise amplification elsewhere to guarantee system robustness. Along the way, we also provide a modern review of model inversion techniques. The proposed algorithm is validated on motion control systems and complex high-order systems.

     

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    • 1 When actuators take forces or torques as the input and linear/angular position as the output, integrator-type plant dynamics with a relative degree not less than two show up.
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    Highlights

    • This optimal inversion bridges accurate model approximations and robust feedback performances.
    • It attains model accuracy at desired frequency regions while constraining noise amplification.
    • The design goals are achieved by a multi-objective H∞ formulation and an all-pass factorization.
    • This paper also provides a modern review of model inversion techniques.
    • The proposed algorithm is validated on motion control systems and complex high-order systems.

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