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<p>This paper proposes a novel data-driven stability enhanced tube-based robust model predictive control (DSE-TRMPC) framework for post-capture control of free-floating space robots (FFSRs) after non-cooperative target capture. The framework systematically addresses coupled uncertainties arising from unknown target dynamics, significant antagonistic disturbances, and input/state measurement noises. Its distinctive architecture comprises two synergistic components: a nominal integral model predictive controller generating the nominal trajectory, and a data-driven stability enhanced (DSE) robustness term that ensures precise disturbance compensation. Key contributions include: (1) a DSE-TRMPC framework that computes the robust term directly from historical input-state data, embedding disturbance/unmodeled dynamics estimation for improved offset-free robustness; (2) a DSE robust term design integrating MTSI with a new 2-ISPC formulation that can enforce second-order input smoothness, thereby mitigating chattering induced by noisy, time-varying identified parameters. The proposed control scheme is proven to guarantee input-to-state stability. Simulation results under both constant and periodic disturbances demonstrate its superior performance in disturbance rejection, state convergence accuracy, and control smoothness compared to existing methods.</p>

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Data-driven stability enhanced tube-based robust model predictive control with disturbance compensation for FFSR post-capture

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Bicheng Cai,
  • Xiaozhe Ju,
  • Yong Zhao

摘要

This paper proposes a novel data-driven stability enhanced tube-based robust model predictive control (DSE-TRMPC) framework for post-capture control of free-floating space robots (FFSRs) after non-cooperative target capture. The framework systematically addresses coupled uncertainties arising from unknown target dynamics, significant antagonistic disturbances, and input/state measurement noises. Its distinctive architecture comprises two synergistic components: a nominal integral model predictive controller generating the nominal trajectory, and a data-driven stability enhanced (DSE) robustness term that ensures precise disturbance compensation. Key contributions include: (1) a DSE-TRMPC framework that computes the robust term directly from historical input-state data, embedding disturbance/unmodeled dynamics estimation for improved offset-free robustness; (2) a DSE robust term design integrating MTSI with a new 2-ISPC formulation that can enforce second-order input smoothness, thereby mitigating chattering induced by noisy, time-varying identified parameters. The proposed control scheme is proven to guarantee input-to-state stability. Simulation results under both constant and periodic disturbances demonstrate its superior performance in disturbance rejection, state convergence accuracy, and control smoothness compared to existing methods.