Model Predictive Force Control in Grinding based on a Lightweight Robot

Husmann, Sonja; Stemmler, Sebastian; Hähnel, Sebastian; Vogelgesang, S.; Abel, Dirk; Bergs, Thomas

doi:10.1016/j.ifacol.2019.11.459

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…In particular, robotics is an emerging field of applications of MPC, e.g. [47,88,134]. While humanoid robots are a special case [134], industrial robots are ubiquitous in the shop floors today.…”

Section: Further Applicationsmentioning

confidence: 99%

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Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

397

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Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

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Section: Further Applicationsmentioning

confidence: 99%

“…Nubert et al [88] improved the tracking robustness in general with a robust MPC. While [47] made use of the force feedback of a lightweight robot to polish the free-form surface of a metal workpiece. The MPC maintained a given pressure on a varying area while moving over the surface.…”

Section: Further Applicationsmentioning

confidence: 99%

Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

397

View full text Add to dashboard Cite

show abstract

“…Although this approach circumvents the need for a potentially complex model of interactions with a priori unknown environments, it comes at the price of only being able to control forces in a feedforward sense. Yet there are solid reasons to believe that closing the loop on force measurements inside a MPC framework could increase the robustness and versatility of robots in contact tasks [4]- [6]. First, the superiority of MPC over instantaneous control methods in online motion generation is now well established [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Introducing Force Feedback in Model Predictive Control

Kleff

Dantec²,

Saurel

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In the literature about model predictive control (MPC), contact forces are planned rather than controlled. In this paper, we propose a novel paradigm to incorporate effort measurements into a predictive controller, hence allowing to control them by direct measurement feedback. We first demonstrate why the classical optimal control formulation, based on position and velocity state feedback, cannot handle direct feedback on force information. Following previous approaches in force control, we then propose to augment the classical formulations with a model of the robot actuation, which naturally allows to generate online trajectories that adapt to sensed position, velocity and torques. We propose a complete implementation of this idea on the upper part of a real humanoid robot, and show through hardware experiments that this new formulation incorporating effort feedback outperforms classical MPC in challenging tasks where physical interaction with the environment is crucial.

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“…Mold polishing is a very important but time-consuming and costly work in the mold manufacturing process [1]. It can remove the convex part of the surface caused by machining and improve the surface quality of the mold.…”

Section: Introductionmentioning

confidence: 99%

A robotic polishing parameter optimization method considering time-varying wear

Zheng

Xiao²,

Wang³

et al. 2022

Int J Adv Manuf Technol

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In the automatic polishing process, the wear of polishing tools and the change of polishing parameters will affect the Preston coefficient, which makes it difficult to establish an accurate material removal model to achieve stable and excellent polishing quality. In this paper, a robotic polishing parameter optimization method considering time-varying wear is proposed to address these issues. First, combining the rich information in the theoretical modeling method with the datadriven regression method, a material removal regression model incorporating prior knowledge is proposed, which greatly reduces the large amount of experimental data required by the original regression model. The proposed model is able to track the wear variation of the sandpaper as well as the effect of polishing parameters. Then, based on the proposed prediction model, the polishing parameters are optimized using the genetic algorithm to achieve better machining quality and less energy consumption. Finally, the experimental verification is carried out on the hybrid robot polishing test bench.The results show that the proposed material removal regression model incorporating prior knowledge has higher prediction accuracy and less required experimental data than existing models. The proposed robot polishing parameter optimization method can effectively compensate for tool wear and ensure consistent material removal during polishing while reducing energy consumption.Keywords Time-varying wear﹒Gaussian process regression﹒Material removal model﹒Genetic Algorithm﹒Parameter optimization Acknowledgements

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Model Predictive Force Control in Grinding based on a Lightweight Robot

Cited by 6 publications

References 10 publications

Review on model predictive control: an engineering perspective

Review on model predictive control: an engineering perspective

Introducing Force Feedback in Model Predictive Control

A robotic polishing parameter optimization method considering time-varying wear

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