Pub Date : 2025-06-26DOI: 10.1016/j.mechatronics.2025.103364
Yongchao Wang , Tian Zheng , Maged Iskandar , Marion Leibold , Jinoh Lee
This article proposes an optimization-based method for robust yet efficient control of flexible-joint robots by using the model predictive control approach. The time-delay estimation (TDE) technique is used to approximate uncertain and nonlinear dynamic equations, where neither concrete knowledge of mathematical system model parameters is required in the approximation, thus granting the model-free property for dynamics compensation and real-time system linearization. TDE is integrated with model predictive control, which is designated as the incremental model predictive control (IMPC) framework. This approach guarantees the tracking performance of the flexible joint robot with input and output constraints, such as motor torque and joint states. Moreover, the proposed controller can practically circumvent high-order derivatives in implementation while providing robust tracking, a capability that conventional methods for flexible joint robots often face challenges due to the inherent nature of their high-order dynamics. The input-to-state stability of IMPC in a local region around the reachable reference trajectory is theoretically proven, and the high approximation accuracy of the resulting incremental system is analyzed. Finally, a series of experiments is conducted on a flexible-joint robot to verify the practical effectiveness of IMPC, and superior performance in terms of high accuracy, high computational efficiency, and constraint admissibility is demonstrated.
{"title":"Practical and robust incremental model predictive control for flexible-joint robots","authors":"Yongchao Wang , Tian Zheng , Maged Iskandar , Marion Leibold , Jinoh Lee","doi":"10.1016/j.mechatronics.2025.103364","DOIUrl":"10.1016/j.mechatronics.2025.103364","url":null,"abstract":"<div><div>This article proposes an optimization-based method for robust yet efficient control of flexible-joint robots by using the model predictive control approach. The time-delay estimation (TDE) technique is used to approximate uncertain and nonlinear dynamic equations, where neither concrete knowledge of mathematical system model parameters is required in the approximation, thus granting the model-free property for dynamics compensation and real-time system linearization. TDE is integrated with model predictive control, which is designated as the incremental model predictive control (IMPC) framework. This approach guarantees the tracking performance of the flexible joint robot with input and output constraints, such as motor torque and joint states. Moreover, the proposed controller can practically circumvent high-order derivatives in implementation while providing robust tracking, a capability that conventional methods for flexible joint robots often face challenges due to the inherent nature of their high-order dynamics. The input-to-state stability of IMPC in a local region around the reachable reference trajectory is theoretically proven, and the high approximation accuracy of the resulting incremental system is analyzed. Finally, a series of experiments is conducted on a flexible-joint robot to verify the practical effectiveness of IMPC, and superior performance in terms of high accuracy, high computational efficiency, and constraint admissibility is demonstrated.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103364"},"PeriodicalIF":3.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-25DOI: 10.1016/j.mechatronics.2025.103378
Kaixian Ba , Ning Liu , Jinbo She , Yuan Wang , Guoliang Ma , Bin Yu , Xiangdong Kong
Accurate position regulation in hydraulic servo systems (HDU) plays a critical role in ensuring system stability, operational efficiency, and achieving high-accuracy performance. However, friction-induced nonlinearities, including Stribeck effects and internal friction dynamics, significantly impact tracking accuracy. This paper introduces a matrix-sensitivity-based active disturbance rejection control (MSADRC) method that compensates for friction without requiring an explicit friction model. By leveraging matrix sensitivity, MSADRC effectively decouples system dynamics and enhances control accuracy, particularly in suppressing frictional effects. A third-order extended state observer (ESO) first estimates total system disturbances, while a model predictive mechanism converts nonlinear time-varying disturbances into a feedforward compensation term. The resulting matrix sensitivity-based compensation optimally adjusts system response, ensuring improved performance. Experimental results show that MSADRC effectively mitigates nonlinear disturbances, reducing peak error by up to 55 % compared to conventional ADRC methods. This approach provides a reliable and efficient strategy to address adaptive friction compensation issues in hydraulic control systems.
{"title":"Matrix-sensitivity-based active disturbance rejection control for hydraulic servo positioning systems with friction compensation","authors":"Kaixian Ba , Ning Liu , Jinbo She , Yuan Wang , Guoliang Ma , Bin Yu , Xiangdong Kong","doi":"10.1016/j.mechatronics.2025.103378","DOIUrl":"10.1016/j.mechatronics.2025.103378","url":null,"abstract":"<div><div>Accurate position regulation in hydraulic servo systems (HDU) plays a critical role in ensuring system stability, operational efficiency, and achieving high-accuracy performance. However, friction-induced nonlinearities, including Stribeck effects and internal friction dynamics, significantly impact tracking accuracy. This paper introduces a matrix-sensitivity-based active disturbance rejection control (MSADRC) method that compensates for friction without requiring an explicit friction model. By leveraging matrix sensitivity, MSADRC effectively decouples system dynamics and enhances control accuracy, particularly in suppressing frictional effects. A third-order extended state observer (ESO) first estimates total system disturbances, while a model predictive mechanism converts nonlinear time-varying disturbances into a feedforward compensation term. The resulting matrix sensitivity-based compensation optimally adjusts system response, ensuring improved performance. Experimental results show that MSADRC effectively mitigates nonlinear disturbances, reducing peak error by up to 55 % compared to conventional ADRC methods. This approach provides a reliable and efficient strategy to address adaptive friction compensation issues in hydraulic control systems.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103378"},"PeriodicalIF":3.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Growing demands in the semiconductor industry necessitate increasingly stringent requirements on throughput and positioning accuracy of lithographic equipment. Meeting these demands involves employing highly aggressive motion profiles, which introduce position-dependent flexible dynamics, thus compromising achievable position tracking performance. This paper introduces a control approach enabling active compensation of position-dependent flexible dynamics by extending the conventional rigid-body control structure to include active control of flexible dynamics. To facilitate real-time implementation of the control algorithm, appropriate position-dependent weighting functions are introduced, ensuring computationally efficient execution of the proposed approach. The efficacy of the proposed control design approach is demonstrated through experiments conducted on a state-of-the-art extreme ultraviolet (EUV) wafer stage.
{"title":"Active compensation of position dependent flexible dynamics in high-precision mechatronics","authors":"Yorick Broens , Hans Butler , Ramidin Kamidi , Koen Verkerk , Siep Weiland","doi":"10.1016/j.mechatronics.2025.103377","DOIUrl":"10.1016/j.mechatronics.2025.103377","url":null,"abstract":"<div><div>Growing demands in the semiconductor industry necessitate increasingly stringent requirements on throughput and positioning accuracy of lithographic equipment. Meeting these demands involves employing highly aggressive motion profiles, which introduce position-dependent flexible dynamics, thus compromising achievable position tracking performance. This paper introduces a control approach enabling active compensation of position-dependent flexible dynamics by extending the conventional rigid-body control structure to include active control of flexible dynamics. To facilitate real-time implementation of the control algorithm, appropriate position-dependent weighting functions are introduced, ensuring computationally efficient execution of the proposed approach. The efficacy of the proposed control design approach is demonstrated through experiments conducted on a state-of-the-art extreme ultraviolet (EUV) wafer stage.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103377"},"PeriodicalIF":3.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-24DOI: 10.1016/j.mechatronics.2025.103374
Andrea Ruo, Luca Bernardi, Ludovico Campanelli, Mattia Grespan, Danila Trane, Roberto Sedoni, Diego Angeli, Lorenzo Sabattini, Valeria Villani
Grasping, carrying, and placing objects are fundamental capabilities and common operations for robots and robotic manipulators. To ensure secure grasping of objects with a wide variety of shapes, sizes, and materials, various sensors and control strategies are also necessary. In this paper, an electromagnetic robotic gripper is proposed. The exploitation of electromagnetism principles for grasping is not new in the literature, but the proposed design innovation aims at proposing an open-source and low-cost solution that can be 3D printed. The developed prototype was tested by performing pick and place operations on samples of progressively increasing mass. Finally, a thermodynamic analysis was conducted to determine the steady-state external temperature of the shell and identify its limitations.
{"title":"A low-cost 3D printed electromagnetic gripper for robotic arms","authors":"Andrea Ruo, Luca Bernardi, Ludovico Campanelli, Mattia Grespan, Danila Trane, Roberto Sedoni, Diego Angeli, Lorenzo Sabattini, Valeria Villani","doi":"10.1016/j.mechatronics.2025.103374","DOIUrl":"10.1016/j.mechatronics.2025.103374","url":null,"abstract":"<div><div>Grasping, carrying, and placing objects are fundamental capabilities and common operations for robots and robotic manipulators. To ensure secure grasping of objects with a wide variety of shapes, sizes, and materials, various sensors and control strategies are also necessary. In this paper, an electromagnetic robotic gripper is proposed. The exploitation of electromagnetism principles for grasping is not new in the literature, but the proposed design innovation aims at proposing an open-source and low-cost solution that can be 3D printed. The developed prototype was tested by performing pick and place operations on samples of progressively increasing mass. Finally, a thermodynamic analysis was conducted to determine the steady-state external temperature of the shell and identify its limitations.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103374"},"PeriodicalIF":3.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliable detection and segmentation of human hands are critical for enhancing safety and facilitating advanced interactions in human–robot collaboration. Current research predominantly evaluates hand segmentation under in-distribution (ID) data, which reflects the training data of deep learning (DL) models. However, this approach fails to address out-of-distribution (OOD) scenarios that often arise in real-world human–robot interactions. In this work, we make three key contributions: first we assess the generalization of deep learning (DL) models for hand segmentation under both ID and OOD scenarios, utilizing a newly collected industrial dataset that captures a wide range of real-world conditions including simple and cluttered backgrounds with industrial tools, varying numbers of hands (0 to 4), gloves, rare gestures, and motion blur. Our second contribution is considering both egocentric and static viewpoints. We evaluated the models trained on four datasets, i.e. EgoHands, Ego2Hands (egocentric mobile camera), HADR, and HAGS (static fixed viewpoint) by testing them with both egocentric (head-mounted) and static cameras, enabling robustness evaluation from multiple points of view. Our third contribution is introducing an uncertainty analysis pipeline based on the predictive entropy of predicted hand pixels. This procedure enables flagging unreliable segmentation outputs by applying thresholds established in the validation phase. This enables automatic identification and filtering of untrustworthy predictions, significantly improving segmentation reliability in OOD scenarios. For segmentation, we used a deep ensemble model composed of UNet and RefineNet as base learners. Our experiments demonstrate that models trained on industrial datasets (HADR, HAGS) outperform those trained on non-industrial datasets, both in segmentation accuracy and in their ability to flag unreliable outputs via uncertainty estimation. These findings underscore the necessity of domain-specific training data and show that our uncertainty analysis pipeline can provide a practical safety layer for real-world deployment.
{"title":"Testing human-hand segmentation on in-distribution and out-of-distribution data in human–robot interactions using a deep ensemble model","authors":"Reza Jalayer , Yuxin Chen , Masoud Jalayer , Carlotta Orsenigo , Masayoshi Tomizuka","doi":"10.1016/j.mechatronics.2025.103365","DOIUrl":"10.1016/j.mechatronics.2025.103365","url":null,"abstract":"<div><div>Reliable detection and segmentation of human hands are critical for enhancing safety and facilitating advanced interactions in human–robot collaboration. Current research predominantly evaluates hand segmentation under in-distribution (ID) data, which reflects the training data of deep learning (DL) models. However, this approach fails to address out-of-distribution (OOD) scenarios that often arise in real-world human–robot interactions. In this work, we make three key contributions: first we assess the generalization of deep learning (DL) models for hand segmentation under both ID and OOD scenarios, utilizing a newly collected industrial dataset that captures a wide range of real-world conditions including simple and cluttered backgrounds with industrial tools, varying numbers of hands (0 to 4), gloves, rare gestures, and motion blur. Our second contribution is considering both egocentric and static viewpoints. We evaluated the models trained on four datasets, i.e. EgoHands, Ego2Hands (egocentric mobile camera), HADR, and HAGS (static fixed viewpoint) by testing them with both egocentric (head-mounted) and static cameras, enabling robustness evaluation from multiple points of view. Our third contribution is introducing an uncertainty analysis pipeline based on the predictive entropy of predicted hand pixels. This procedure enables flagging unreliable segmentation outputs by applying thresholds established in the validation phase. This enables automatic identification and filtering of untrustworthy predictions, significantly improving segmentation reliability in OOD scenarios. For segmentation, we used a deep ensemble model composed of UNet and RefineNet as base learners. Our experiments demonstrate that models trained on industrial datasets (HADR, HAGS) outperform those trained on non-industrial datasets, both in segmentation accuracy and in their ability to flag unreliable outputs via uncertainty estimation. These findings underscore the necessity of domain-specific training data and show that our uncertainty analysis pipeline can provide a practical safety layer for real-world deployment.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103365"},"PeriodicalIF":3.1,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-20DOI: 10.1016/j.mechatronics.2025.103366
Mirado Rajaomarosata, Luc Jaulin, Lionel Lapierre, Simon Rohou
Bio-inspired robots remain far less energy-efficient than animals because conventional controllers impose trajectories that fight passive dynamics, whereas animals exploit resonance through natural nonlinear normal modes (NNM), whose periodic internal motions form a smooth 2D invariant surface; We ask how to define and compute the natural motions of a conservative locomotion system: propulsion arises only from no-slip constraints, and once initiated, a gait persists without actuation—like a frictionless pendulum. We tackle non-holonomic constraints on the Pendrivencar, a vehicle driven by a motorised pendulum with a cubic torsional spring; We introduce the Nonholonomic Locomotion - NNM (NL-NNM): extract a high-speed spectral seed – where chassis oscillations vanish and the pendulum is neutrally stable – refine the periodic orbit, and continue the resulting 2D invariant manifold via pseudo-arclength across three slow centre manifolds (stable for positive speed, neutral at zero, unstable for negative) from non-isolated rectilinear equilibria; We demonstrate the first NL-NNM for a moving non-holonomic robot: internal orbits produce a pendulum–chassis choreography whose energy-dependent frequency shifts and harmonic richness exceed linear predictions. Via geometric phase, each orbit yields undulatory straight-line motion. A dual-loop control simulation confirms autonomous path tracking with only the pendulum; Extending to dissipative regimes via non-linear resonant modes offers a path to high-efficiency locomotion in aquatic, aerial, legged, soft-bodied, and other robots.
{"title":"Natural efficient gaits from Nonholonomic Locomotion Nonlinear Normal Mode (NL-NNM): The Pendrivencar case","authors":"Mirado Rajaomarosata, Luc Jaulin, Lionel Lapierre, Simon Rohou","doi":"10.1016/j.mechatronics.2025.103366","DOIUrl":"10.1016/j.mechatronics.2025.103366","url":null,"abstract":"<div><div>Bio-inspired robots remain far less energy-efficient than animals because conventional controllers impose trajectories that fight passive dynamics, whereas animals exploit resonance through <em>natural nonlinear normal modes (NNM)</em>, whose periodic internal motions form a smooth 2D invariant surface; We ask how to define and compute the <em>natural motions of a conservative locomotion system</em>: propulsion arises only from <em>no-slip constraints</em>, and once initiated, a gait persists without actuation—like a frictionless pendulum. We tackle non-holonomic constraints on the <em>Pendrivencar</em>, a vehicle driven by a <em>motorised pendulum with a cubic torsional spring</em>; We introduce the <strong>Nonholonomic Locomotion - NNM (NL-NNM)</strong>: extract a <em>high-speed spectral seed</em> – where chassis oscillations vanish and the pendulum is neutrally stable – refine the periodic orbit, and continue the resulting <em>2D invariant manifold</em> via pseudo-arclength across <em>three slow centre manifolds</em> (stable for positive speed, neutral at zero, unstable for negative) from non-isolated rectilinear equilibria; We demonstrate the first NL-NNM for a moving non-holonomic robot: internal orbits produce a <em>pendulum–chassis choreography</em> whose <em>energy-dependent frequency shifts</em> and <em>harmonic richness</em> exceed linear predictions. Via <em>geometric phase</em>, each orbit yields undulatory straight-line motion. A <em>dual-loop control simulation</em> confirms autonomous path tracking with only the pendulum; Extending to dissipative regimes via <em>non-linear resonant modes</em> offers a path to high-efficiency locomotion in aquatic, aerial, legged, soft-bodied, and other robots.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103366"},"PeriodicalIF":3.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-19DOI: 10.1016/j.mechatronics.2025.103359
G. Wang , R. Chalard , J.A. Cifuentes , M.T. Pham
Pneumatic Artificial Muscles (PAMs) are highly nonlinear actuators widely used in robotics, rehabilitation, and other dynamic applications. Their complex behavior poses significant challenges for traditional system identification methods. Although machine learning techniques have shown remarkable success in modeling nonlinear systems, their black-box nature often leads to interpretability issues and susceptibility to overfitting. This study proposes a novel hybrid modeling approach that combines the strengths of analytical models with neural networks to capture the inverse thermodynamic behavior of PAMs. The results demonstrate that the hybrid model outperformed both analytical and purely neural network models. The obtained models were further used for model-based control design and the results show that the application of hybrid model improved the tracking performance.
{"title":"Learning an inverse thermodynamic model for Pneumatic Artificial Muscles control","authors":"G. Wang , R. Chalard , J.A. Cifuentes , M.T. Pham","doi":"10.1016/j.mechatronics.2025.103359","DOIUrl":"10.1016/j.mechatronics.2025.103359","url":null,"abstract":"<div><div>Pneumatic Artificial Muscles (PAMs) are highly nonlinear actuators widely used in robotics, rehabilitation, and other dynamic applications. Their complex behavior poses significant challenges for traditional system identification methods. Although machine learning techniques have shown remarkable success in modeling nonlinear systems, their black-box nature often leads to interpretability issues and susceptibility to overfitting. This study proposes a novel hybrid modeling approach that combines the strengths of analytical models with neural networks to capture the inverse thermodynamic behavior of PAMs. The results demonstrate that the hybrid model outperformed both analytical and purely neural network models. The obtained models were further used for model-based control design and the results show that the application of hybrid model improved the tracking performance.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103359"},"PeriodicalIF":3.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-19DOI: 10.1016/j.mechatronics.2025.103360
Xuan Dung To , Jefferson Roman Blanco , Sandra Zimmer-Chevret , Ghinwa Ouaidat , Thibaut Raharijaona , Farid Noureddine , Micky Rakotondrabe
In Robotized Incremental Sheet Forming (ISF), achieving precise geometrical accuracy is a challenging task due to trajectory tool center point (TCP) position errors at the forming tool attached to the robot’s end-effector. These errors primarily arise from external disturbance forces and torques generated during the interaction between the forming tool and the elastic metal sheet. While joint-torque space controllers can mitigate reaction forces and torques through dynamic modeling, joint-space control has inherent limitations, particularly for industrial high-load robots like the ABB IRB 8700. To overcome these challenges, this work implements an external force/torque (F/T) compensator in task-space using a deep neural network. The network predicts trajectory errors induced by reaction forces and torques measured via a 6-axis F/T sensor. Additionally, the forming tool’s trajectory is precisely monitored using a laser tracker, which serves as a feedback mechanism in a closed-loop task-space error-tracking controller. This controller detects and corrects trajectory deviations in real time. By integrating the F/T compensator and the task-space error-tracking controller, the proposed approach effectively compensates for reaction forces and torques while addressing additional errors introduced by other process-related factors. This integration results in significantly enhanced accuracy in robotic incremental forming processes.
{"title":"Robotized Incremental Sheet Forming trajectory control using deep neural network for force/torque compensator and task-space error tracking controller","authors":"Xuan Dung To , Jefferson Roman Blanco , Sandra Zimmer-Chevret , Ghinwa Ouaidat , Thibaut Raharijaona , Farid Noureddine , Micky Rakotondrabe","doi":"10.1016/j.mechatronics.2025.103360","DOIUrl":"10.1016/j.mechatronics.2025.103360","url":null,"abstract":"<div><div>In Robotized Incremental Sheet Forming (ISF), achieving precise geometrical accuracy is a challenging task due to trajectory tool center point (TCP) position errors at the forming tool attached to the robot’s end-effector. These errors primarily arise from external disturbance forces and torques generated during the interaction between the forming tool and the elastic metal sheet. While joint-torque space controllers can mitigate reaction forces and torques through dynamic modeling, joint-space control has inherent limitations, particularly for industrial high-load robots like the ABB IRB 8700. To overcome these challenges, this work implements an external force/torque (F/T) compensator in task-space using a deep neural network. The network predicts trajectory errors induced by reaction forces and torques measured via a 6-axis F/T sensor. Additionally, the forming tool’s trajectory is precisely monitored using a laser tracker, which serves as a feedback mechanism in a closed-loop task-space error-tracking controller. This controller detects and corrects trajectory deviations in real time. By integrating the F/T compensator and the task-space error-tracking controller, the proposed approach effectively compensates for reaction forces and torques while addressing additional errors introduced by other process-related factors. This integration results in significantly enhanced accuracy in robotic incremental forming processes.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103360"},"PeriodicalIF":3.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1016/j.mechatronics.2025.103362
Sucai Zhang , Yongfu Wang , Gang Li
A finite time adaptive output feedback control scheme with state constraint is proposed for the path tracking control of autonomous vehicle considering the asymmetric dead-zone. Firstly, the vehicle dynamics model and path tracking model are established by combining the dead-zone model, and the adaptive law is designed to approximate the parameters of dead-zone model. On this basis, an adaptive backstepping controller with output-constrained feedback control is designed by combining the filtering error compensation mechanism and the finite time technique, introducing the barrier Lyapunov function and the backstepping control technique. In order to save communication resources, a dynamic threshold event triggering mechanism is introduced. Finally, a rigorous stability analysis based on Lyapunov stability theory is presented to ensure that all signals of the closed-loop system are bounded in finite time. The effectiveness of the proposed method is verified by different simulations, hardware-in-the-loop experiments and real-time vehicle experiments. The results show that the proposed method is effective under different working conditions. The results of real-time vehicle experiments show that the controller can effectively improve the accuracy of path tracking control and reduce the maximum lateral position error to 0.1752 m compared with other methods, and the scheme can provide a theoretical reference for the control practice of autonomous vehicle.
{"title":"Adaptive backstepping finite-time output feedback control for path tracking of autonomous vehicle with asymmetric dead-zone","authors":"Sucai Zhang , Yongfu Wang , Gang Li","doi":"10.1016/j.mechatronics.2025.103362","DOIUrl":"10.1016/j.mechatronics.2025.103362","url":null,"abstract":"<div><div>A finite time adaptive output feedback control scheme with state constraint is proposed for the path tracking control of autonomous vehicle considering the asymmetric dead-zone. Firstly, the vehicle dynamics model and path tracking model are established by combining the dead-zone model, and the adaptive law is designed to approximate the parameters of dead-zone model. On this basis, an adaptive backstepping controller with output-constrained feedback control is designed by combining the filtering error compensation mechanism and the finite time technique, introducing the barrier Lyapunov function and the backstepping control technique. In order to save communication resources, a dynamic threshold event triggering mechanism is introduced. Finally, a rigorous stability analysis based on Lyapunov stability theory is presented to ensure that all signals of the closed-loop system are bounded in finite time. The effectiveness of the proposed method is verified by different simulations, hardware-in-the-loop experiments and real-time vehicle experiments. The results show that the proposed method is effective under different working conditions. The results of real-time vehicle experiments show that the controller can effectively improve the accuracy of path tracking control and reduce the maximum lateral position error to 0.1752 m compared with other methods, and the scheme can provide a theoretical reference for the control practice of autonomous vehicle.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103362"},"PeriodicalIF":3.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-16DOI: 10.1016/j.mechatronics.2025.103363
Yin Sun, Feng Zhao, Zhenjing Guo, Xiaojun Yan
The parallel-suspension type inertially stabilized platform utilizes a unique flexible support structure and non-contact linear actuators to enable simultaneous high-efficiency vibration suppression control of optical payloads across multiple degrees of freedom. Compared to traditional series – gimbals type stabilized platforms, it offers a higher payload-to-weight ratio and rapid response characteristics. In this paper, a 6-degree-of-freedom dynamic model for the parallel-suspension inertially stabilized platform is established, a control method is designed, and an actual engineering prototype is constructed. Specifically, a flexible support element model that accounts for column instability phenomenon is developed. Based on the parallel mount configuration a complete 6-degree-of-freedom dynamic model of the entire platform is constructed. Furthermore, due the variable parameter characteristics of flexible elastic elements, a μ synthesis control method considering the uncertainty of model parameters is designed. The experimental results show that the μ controller can effectively reduce the external sinusoidal angular disturbance to less than 25 % and the linear vibration disturbance to less than 3 % of the original disturbance while maintaining the robustness. Both simulation and experimental results verify the correctness and effectiveness of the proposed model and method.
{"title":"Dynamics modeling and μ synthesis for a parallel - suspension type inertially stabilized platform","authors":"Yin Sun, Feng Zhao, Zhenjing Guo, Xiaojun Yan","doi":"10.1016/j.mechatronics.2025.103363","DOIUrl":"10.1016/j.mechatronics.2025.103363","url":null,"abstract":"<div><div>The parallel-suspension type inertially stabilized platform utilizes a unique flexible support structure and non-contact linear actuators to enable simultaneous high-efficiency vibration suppression control of optical payloads across multiple degrees of freedom. Compared to traditional series – gimbals type stabilized platforms, it offers a higher payload-to-weight ratio and rapid response characteristics. In this paper, a 6-degree-of-freedom dynamic model for the parallel-suspension inertially stabilized platform is established, a control method is designed, and an actual engineering prototype is constructed. Specifically, a flexible support element model that accounts for column instability phenomenon is developed. Based on the parallel mount configuration a complete 6-degree-of-freedom dynamic model of the entire platform is constructed. Furthermore, due the variable parameter characteristics of flexible elastic elements, a <em>μ</em> synthesis control method considering the uncertainty of model parameters is designed. The experimental results show that the <em>μ</em> controller can effectively reduce the external sinusoidal angular disturbance to less than 25 % and the linear vibration disturbance to less than 3 % of the original disturbance while maintaining the robustness. Both simulation and experimental results verify the correctness and effectiveness of the proposed model and method.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103363"},"PeriodicalIF":3.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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