Sequential and Iterative Distributed Model Predictive Control of Multi-Motor Driving Cutterhead System for TBM

Yang, Xiaofeng; Zhang, Langwen; Xie, Wei; Zhang, Junfeng

doi:10.1109/access.2019.2908388

Cited by 23 publications

(8 citation statements)

References 44 publications

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“…Very few approaches exist for torque control of MMDS with substantial limitations. E.g., [17] only selects steady-state torque references using highly simplified drive models also neglecting the IM dynamics and [18] considers the dynamic torque references for complex mechanical loads without taking the efficiency issue in steady-state operation into account.…”

Section: A State Of the Artmentioning

confidence: 99%

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Becker,

Stender,

Wallscheid

2023

Preprint

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<p>Induction machines (IMs) are widely used in many applications, e.g., electric vehicles or industrial automation, which motivates efficiency-optimal operation for the sake of energy and cost savings. However, a loss-minimal IM control leads to reduced flux operation at partial load, i.e., achievable torque dynamics after load steps are physically limited due to the IM’s large rotor time constant. Hence, designing the IM torque control for minimal settling time is of particular importance to allow both an efficient and sufficiently dynamic drive operation. Against this background, a model predictive control (MPC) framework is proposed, which utilizes a precise model of the IM covering magnetic saturation, iron losses, skin effect and thermal influences. As this model is highly nonlinear, it is iteratively linearized along the predicted control trajectory so that a computationally efficient quadratic program can be defined for the MPC problem. To enable sufficiently long prediction horizons, a hierarchical control structure with the stator currents as the actuation variables of the model predictive torque control is utilized. Thanks to this scalable control approach, the proposed framework can be easily extended to multi-machine drive systems covering higher-dimensional problem spaces. Empirical tests validate the feasibility of the proposed approach both for single as well as multi-machine drive applications.</p>

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Section: A State Of the Artmentioning

confidence: 99%

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Becker,

Stender,

Wallscheid

2023

Preprint

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“…High-power density transmission box system has the advantages of compact structure, and precise and efficient transmission, which is widely used in the transmission equipment of vehicles, ships and aviation [1][2][3]. However, it is easy to deform or vibrate in processing due to the box-shaped part structure with lower stiffness.…”

Section: Introductionmentioning

confidence: 99%

A Method for Predicting the Boring Topography Error of Shaft Hole on a Thin-Walled Box Based on a Proposed Dynamic Model

2023

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Traditional boring dynamic model ignores the effect of the uneven radial stiffness of the shaft hole of the thin-walled box on the boring vibration characteristic, which will lead to the deviation in the prediction for both the cutting force and the surface topography of the machining hole. To address this shortcoming, a new calculation model of the equivalent radial stiffness of the shaft hole is proposed and then validated by the finite element (FE) method. On this basis, a boring dynamic model of the shaft hole is developed, and an experimental study on the dynamic characteristics of the shaft hole boring is carried out to verify the correctness and effectiveness of the proposed dynamic model. Moreover, a prediction method for the surface topography error of the machined shaft hole is presented based on the proposed dynamic model. Finally, the comparison investigation on the hole machined surface topography predicted by the proposed method and measured in practice is carried out. The results show that the proposed theoretical models and method can achieve accurate predictions for both the boring cutting force and the machined surface topography error of the shaft hole on the thin-walled box. This study can provide a theoretical basis for machining error control and process optimization of the fine boring of the thin-walled transmission box.INDEX TERMS Thin-walled box, shaft hole machining, boring dynamic model, surface topography prediction.

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“…However, this paper does not discuss about how these performances can be utilized for TBM control. Yang et al (2019) designed a distributed model predictive control scheme to implement the tracking control of TBM, where the performance only considered about the error between actual trajectory and reference as like the common setting in model predictive control. The works in Zhao et al (2015) introduced a fuzzy Proportion Integration Differentiation (PID) controller for each induction motor.…”

Section: Introductionmentioning

confidence: 99%

The multi-objective optimization of tunneling boring machine control based on geological conditions identification

Wang

2020

JIMSE

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PurposeThe purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability.Design/methodology/approachThis paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end.FindingsThis paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data.Originality/valueThis paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.

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Sequential and Iterative Distributed Model Predictive Control of Multi-Motor Driving Cutterhead System for TBM

Cited by 23 publications

References 44 publications

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

A Method for Predicting the Boring Topography Error of Shaft Hole on a Thin-Walled Box Based on a Proposed Dynamic Model

The multi-objective optimization of tunneling boring machine control based on geological conditions identification

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