Offset-free Input-Output Formulations of Stochastic Model Predictive Control Based on Polynomial Chaos Theory

Andrian, Matthias von; Braatz, Richard D.

doi:10.23919/acc.2019.8814366

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…1. We consider time-variant uncertainty, in contrast to prior work in stochastic QDMC that was focusing on time-invariant uncertainty [15], [16]. While one way to deal with uncertainty is by means of robust predictive controllers [17], it is known that these approaches are often too conservative, and degrade the overall performance by considering unlikely scenarios with equal importance to the most likely ones [15].…”

Section: Introductionmentioning

confidence: 99%

Quadratic Dynamic Matrix Control for Fast Cloth Manipulation

Caldarelli,

Colomé,

Ocampo-Martinez

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Robotic cloth manipulation is an increasingly relevant area of research, challenging classic control algorithms due to the deformable nature of cloth. While it is possible to apply linear model predictive control to make the robot move the cloth according to a given reference, this approach suffers from a large dimensionality of the state-space representation of the cloth models. To address this issue, in this work we study the application of an input-output model predictive control strategy, based on quadratic dynamic matrix control, to robotic cloth manipulation. To account for uncertain disturbances on the cloth's motion, we further extend the algorithm with suitable chance constraints. In extensive simulated experiments, involving disturbances and obstacle avoidance, we show that quadratic dynamic matrix control can be successfully applied in different cloth manipulation scenarios, with significant gains in optimization speed compared to standard model predictive control strategies. The experiments further demonstrate that the closed-loop model used by quadratic dynamic matrix control can be beneficial to the tracking accuracy, leading to improvements over the standard predictive control strategy. Moreover, a preliminary experiment on a real robot shows that quadratic dynamic matrix control can indeed be employed in real settings.

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