Time-optimized model predictive control for tendon-driven variable-stiffness origami continuum robots: Implementation, simulations and experiments.

Authors

Shuwen Pang, Hui Chen, Lingyun Yao

Published in

ISA transactions. Nov 10, 2026. Epub Nov 10, 2026.

Abstract

To address the safety and rapid control challenges posed by nonlinear constraints in variable-stiffness robots, this paper proposes a time-optimized model predictive control (TOMPC) scheme for rapid, safe, and precise trajectory tracking. The nonlinear stiffness constraints are explicitly mapped into the tendon actuation space as dynamic constraints. By incorporating a bounded scaling factor, the control problem is formulated as a nonlinear programming problem and solved in real time via a sequential quadratic programming solver. The proposed scheme is validated on a tendon-driven variable-stiffness origami continuum robot with a stiffness adjustment range of over 495%. Compared with mainstream kinematic control schemes such as MPC and QP, the TOMPC scheme increases trajectory coverage by approximately 500% within 5 s for circular tracking, while maintaining position errors within 19 mm. Consequently, the proposed TOMPC scheme simultaneously achieves predictive control, time‑optimized objectives, and compliance with complex physical constraints.

PMID:
43034106
Bibliographic data and abstract were imported from PubMed on 15 Nov 2026.

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