Characteristic Model-Based Discrete Adaptive Integral SMC for Robotic Joint Drive on Dual-Core ARM

Addressing escalating demands for high-precision compact robotic actuators, this study overcomes persistent challenges from nonlinear transmission dynamics and computational constraints through a co-designed framework integrating three innovations. A real-time second-order characteristic modeling approach enables 10 kHz online parameter identification, reducing computational load by 13.1% versus MPC. Building on this foundation, a hybrid integral sliding-mode controller eliminating modeling errors while maintaining ≤0.25 rad/s tracking error (SRMSE) under variable loads was created. These algorithmic advances are embedded within a miniaturized dual-ARM platform (47 × 47 × 12 mm3) achieving <30-ns overcurrent protection and 36% cost reduction versus DSP/FPGA solutions. Validated via Lyapunov stability proofs and experiments, this framework is particularly effective for high-performance robotic joint control in spatially- and thermally-constrained environments while dynamically compensating for unmodeled nonlinearities.

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Publication Details

Journal
Symmetry
Published
2025-09-03
DOI
https://doi.org/10.3390/sym17091436
Primary Topic
Iterative Learning Control Systems
Type
article
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article

Characteristic Model-Based Discrete Adaptive Integral SMC for Robotic Joint Drive on Dual-Core ARM

Chen Wei
Symmetry
Iterative Learning Control Systems
article

Characteristic Model-Based Discrete Adaptive Integral SMC for Robotic Joint Drive on Dual-Core ARM

Chen Wei
article en

Abstract

Addressing escalating demands for high-precision compact robotic actuators, this study overcomes persistent challenges from nonlinear transmission dynamics and computational constraints through a co-designed framework integrating three innovations. A real-time second-order characteristic modeling approach enables 10 kHz online parameter identification, reducing computational load by 13.1% versus MPC. Building on this foundation, a hybrid integral sliding-mode controller eliminating modeling errors while maintaining ≤0.25 rad/s tracking error (SRMSE) under variable loads was created. These algorithmic advances are embedded within a miniaturized dual-ARM platform (47 × 47 × 12 mm3) achieving <30-ns overcurrent protection and 36% cost reduction versus DSP/FPGA solutions. Validated via Lyapunov stability proofs and experiments, this framework is particularly effective for high-performance robotic joint control in spatially- and thermally-constrained environments while dynamically compensating for unmodeled nonlinearities.

SymmetryVol. 17(9)
Nanjing University of Industry Technology (CN)
Openalex Percentile: Top 72%
Iterative Learning Control Systems
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