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Disturbance Observer-Based Model Predictive Control for Cable-Driven Parallel Robots
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DOI：10.1109/TRO.2025.3626664.png)
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Abstract
En 中文
In this article, a disturbance observer-based model predictive control (DOB-based MPC) strategy is proposed for the trajectory tracking of cable-driven parallel robots (CDPRs). The original nonlinear optimization problem of the MPC explicitly handles the positive bounded constraints of cable tensions and is transformed into a quadratic problem (QP) based on the desired trajectory and offline workspace analysis. Additionally, the uncertainties and external disturbances in the system are considered and derived as the lumped disturbance. Then, a nonlinear DOB is used to estimate the lumped disturbance, and accordingly the estimation is used to enhance the prediction model of the MPC. The control input of the proposed MPC strategy is redesigned to incorporate an auxiliary controller. The estimation error of the DOB and the time-varying characteristics of the disturbance are leveraged to tighten the constraints of the QP less conservatively and generate feasible tubes. Such tube techniques guarantee the recursive feasibility and the input-to-state stability of the proposed MPC strategy. Furthermore, the whole algorithm for deployment including an online constraint updating method is developed. Both simulations and experiments are carried out thoroughly, showing that the DOB-based MPC can effectively improve trajectory tracking accuracy and ensure that the cable tensions satisfy the constraints in the case of unknown disturbances and model uncertainties.
Keywords:
Cable-driven parallel robots (CDPRs)
disturbance observer
model predictive control (MPC)
tube techniques
Journal
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10.5
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3.3K
Citations:
2.8W