Pub Date : 2025-06-14DOI: 10.1016/j.mechatronics.2025.103358
Bruno S.S. Pereira , Tito L.M. Santos , Andre G.S. Conceicao
This paper proposes a new robust feedback-linearization MPC for a class of Unmanned Ground Vehicles. A robust MPC for trajectory tracking with an artificial target is combined with a suitable constraint mapping to ensure robust constraint satisfaction and recursive feasibility despite the effect of bounded disturbances. The artificial reference provides a potentially enlarged domain of attraction, and an analytical target modification is used to achieve the convergence of the tracking error to a minimal robust positively invariant set. The feedback-linearization trade-off concerning the transformed constraints is also analyzed. A case study demonstrating the control strategy’s performance is presented using the Clearpath Husky A200 UGV and the OptiTrack motion capture system.
{"title":"A robust feedback-linearization MPC with artificial target for UGVs","authors":"Bruno S.S. Pereira , Tito L.M. Santos , Andre G.S. Conceicao","doi":"10.1016/j.mechatronics.2025.103358","DOIUrl":"10.1016/j.mechatronics.2025.103358","url":null,"abstract":"<div><div>This paper proposes a new robust feedback-linearization MPC for a class of Unmanned Ground Vehicles. A robust MPC for trajectory tracking with an artificial target is combined with a suitable constraint mapping to ensure robust constraint satisfaction and recursive feasibility despite the effect of bounded disturbances. The artificial reference provides a potentially enlarged domain of attraction, and an analytical target modification is used to achieve the convergence of the tracking error to a minimal robust positively invariant set. The feedback-linearization trade-off concerning the transformed constraints is also analyzed. A case study demonstrating the control strategy’s performance is presented using the Clearpath Husky A200 UGV and the OptiTrack motion capture system.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103358"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289012","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}
Distributed drive electric vehicles actuated by in-wheel motors and brake-by-wire systems enable tracking target motion while improving extra vehicle performance. Outboard brake torque allocated on front and rear wheels generates diverse vertically reactive anti-dive forces, providing an innovative approach to mitigate brake dive without requiring active suspensions. However, the differing dynamics of regenerative and hydraulic braking, along with multiple uncertain vehicle parameters, pose significant challenges to achieving robustness under mixed uncertainties. Moreover, pitch-induced bias in onboard acceleration measurements further degrades control accuracy. To address above problems, this paper proposes a robust, comfort-enhanced longitudinal control system with coordinated braking. A three-degree-of-freedom vehicle dynamics model is developed to incorporate the effect of anti-dive forces. For accurate feedback, a robust observer is designed to compensate pitch-variation-related acceleration measurement biases. By integrating dynamic and parametric uncertainties into the control-oriented model, the mixed -synthesis is employed to design a two-degree-of-freedom controller to robustly optimize the acceleration tracking and anti-dive performance. Compared to the controller designed by standard -synthesis, the proposed approach achieves a 10% improvement in robust performance. Real-vehicle experiments validate the system’s effectiveness, demonstrating over a 27% reduction in pitch angle while maintaining satisfactory acceleration responses under blended braking conditions.
{"title":"Comfort-enhanced longitudinal control for DDEVs: A robust brake coordination approach leveraging reactive anti-dive forces","authors":"Yanjun Ren , Tong Shen , Mingzhuo Zhao , Fanxun Wang , Liwei Xu , Guodong Yin","doi":"10.1016/j.mechatronics.2025.103357","DOIUrl":"10.1016/j.mechatronics.2025.103357","url":null,"abstract":"<div><div>Distributed drive electric vehicles actuated by in-wheel motors and brake-by-wire systems enable tracking target motion while improving extra vehicle performance. Outboard brake torque allocated on front and rear wheels generates diverse vertically reactive anti-dive forces, providing an innovative approach to mitigate brake dive without requiring active suspensions. However, the differing dynamics of regenerative and hydraulic braking, along with multiple uncertain vehicle parameters, pose significant challenges to achieving robustness under mixed uncertainties. Moreover, pitch-induced bias in onboard acceleration measurements further degrades control accuracy. To address above problems, this paper proposes a robust, comfort-enhanced longitudinal control system with coordinated braking. A three-degree-of-freedom vehicle dynamics model is developed to incorporate the effect of anti-dive forces. For accurate feedback, a robust <span><math><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></mrow></math></span> observer is designed to compensate pitch-variation-related acceleration measurement biases. By integrating dynamic and parametric uncertainties into the control-oriented model, the mixed <span><math><mi>μ</mi></math></span>-synthesis is employed to design a two-degree-of-freedom controller to robustly optimize the acceleration tracking and anti-dive performance. Compared to the controller designed by standard <span><math><mi>μ</mi></math></span>-synthesis, the proposed approach achieves a 10% improvement in robust performance. Real-vehicle experiments validate the system’s effectiveness, demonstrating over a 27% reduction in pitch angle while maintaining satisfactory acceleration responses under blended braking conditions.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103357"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281075","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 : 2025-06-11DOI: 10.1016/j.mechatronics.2025.103356
Joseph Nofech, Mir Behrad Khamesee
This study presents a novel methodology for achieving three-degree-of-freedom (3-DoF) control for an attractive-type magnetically-levitated (maglev) microrobot using machine learning. Contact micromanipulation methods face challenges associated with friction, backlash, and maintenance requirements; particularly in delicate applications such as cell injection. The frictionless and low-maintenance nature of attractive-type maglev makes it a viable alternative to traditional methods, but achieving precise 3-DoF control for such systems is not straightforward due to the complexity of their magnetic fields. This research addresses this problem by introducing a machine learning-based methodology that automates the learning of levitation dynamics across the workspace, effectively bypassing a major challenge associated with cross-disciplinary applications of attractive-type maglev.
Our presented approach introduces an automated system for generating training data with minimal human intervention, allowing a machine learning model to quantify the levitated microrobot’s physical response to system inputs while accounting for position-dependent variations in levitation dynamics across the workspace. This model is then used to establish 3-DoF position control of the levitated microrobot. In addition to simplifying the setup process for new and newly-modified attractive-type levitation platforms, this new data-driven methodology is demonstrated to improve performance over conventional methods; achieving up to a 20% reduction in root mean square error during trajectory tracking and up to a 36% reduction in step response settling times.
The results demonstrate the ability of our automated methodology to significantly reduce the accessibility barriers associated with establishing and modifying attractive-type maglev platforms; effectively replacing the usual methods of finite element simulation, precise magnetic field measurements, and/or analytical calculations while providing enhanced levitation control over traditional methods. This advancement contributes to the field of micromanipulation and microforce sensing by offering a more accessible and efficient approach to achieving precise control in attractive-type maglev systems.
{"title":"Machine learning for automation of 3-DoF control of magnetically-levitated microrobots","authors":"Joseph Nofech, Mir Behrad Khamesee","doi":"10.1016/j.mechatronics.2025.103356","DOIUrl":"10.1016/j.mechatronics.2025.103356","url":null,"abstract":"<div><div>This study presents a novel methodology for achieving three-degree-of-freedom (3-DoF) control for an attractive-type magnetically-levitated (maglev) microrobot using machine learning. Contact micromanipulation methods face challenges associated with friction, backlash, and maintenance requirements; particularly in delicate applications such as cell injection. The frictionless and low-maintenance nature of attractive-type maglev makes it a viable alternative to traditional methods, but achieving precise 3-DoF control for such systems is not straightforward due to the complexity of their magnetic fields. This research addresses this problem by introducing a machine learning-based methodology that automates the learning of levitation dynamics across the workspace, effectively bypassing a major challenge associated with cross-disciplinary applications of attractive-type maglev.</div><div>Our presented approach introduces an automated system for generating training data with minimal human intervention, allowing a machine learning model to quantify the levitated microrobot’s physical response to system inputs while accounting for position-dependent variations in levitation dynamics across the workspace. This model is then used to establish 3-DoF position control of the levitated microrobot. In addition to simplifying the setup process for new and newly-modified attractive-type levitation platforms, this new data-driven methodology is demonstrated to improve performance over conventional methods; achieving up to a 20% reduction in root mean square error during trajectory tracking and up to a 36% reduction in step response settling times.</div><div>The results demonstrate the ability of our automated methodology to significantly reduce the accessibility barriers associated with establishing and modifying attractive-type maglev platforms; effectively replacing the usual methods of finite element simulation, precise magnetic field measurements, and/or analytical calculations while providing enhanced levitation control over traditional methods. This advancement contributes to the field of micromanipulation and microforce sensing by offering a more accessible and efficient approach to achieving precise control in attractive-type maglev systems.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103356"},"PeriodicalIF":3.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264135","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 : 2025-06-07DOI: 10.1016/j.mechatronics.2025.103353
Michael Pumphrey , Almuatazbellah M. Boker , Mohammad Al Saaideh , Natheer Alatawneh , Yazan M. Al-Rawashdeh , Khaled Aljanaideh , Mohammad Al Janaideh
A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesired vibrations and disturbances on motion stages. To address this, a higher-dimensional state-space model with nonlinear observable functions is developed to approximate the cable slab dynamics. The proposed model achieves a prediction error within % over the specified motion range and demonstrates robustness in predicting untrained, randomized, acyclic cable slab motions. A systematic evaluation of various observable functions was conducted to minimize the modeling errors, leading to an optimized model with fractional-order exponents. When compared with a neural network-based state-space model (NN-SS), the Koopman approach demonstrated faster training and better performance. For force prediction, the Koopman approach achieved a reduction of three-quarters in maximum error when compared with the NN-SS method. This work offers a concise and experimentally validated analytical framework specifically for developing accurate predictive models of nonlinear cable slab dynamics.
{"title":"Modeling and prediction of nonlinear cable slab dynamics using Koopman operators","authors":"Michael Pumphrey , Almuatazbellah M. Boker , Mohammad Al Saaideh , Natheer Alatawneh , Yazan M. Al-Rawashdeh , Khaled Aljanaideh , Mohammad Al Janaideh","doi":"10.1016/j.mechatronics.2025.103353","DOIUrl":"10.1016/j.mechatronics.2025.103353","url":null,"abstract":"<div><div>A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesired vibrations and disturbances on motion stages. To address this, a higher-dimensional state-space model with nonlinear observable functions is developed to approximate the cable slab dynamics. The proposed model achieves a prediction error within <span><math><mrow><mo>∼</mo><mn>1</mn></mrow></math></span>% over the specified motion range and demonstrates robustness in predicting untrained, randomized, acyclic cable slab motions. A systematic evaluation of various observable functions was conducted to minimize the modeling errors, leading to an optimized model with fractional-order exponents. When compared with a neural network-based state-space model (NN-SS), the Koopman approach demonstrated faster training and better performance. For force prediction, the Koopman approach achieved a reduction of three-quarters in maximum error when compared with the NN-SS method. This work offers a concise and experimentally validated analytical framework specifically for developing accurate predictive models of nonlinear cable slab dynamics.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103353"},"PeriodicalIF":3.1,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230252","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}
To reduce residual vibration with accurate positioning for a flexible hydraulic manipulator, this paper proposes a dual-impulse vibration suppression method to implement concrete pumping tasks. Through sealing up the load-bearing chamber and allow fluid exchange in the non-bearing chamber by individual metering control (IMC), a valve-based volume control method without position sensors is proposed to replace direct positioning control of the end point. Besides, a dual-impulse valve controller is designed for making an online tradeoff between vibration suppression and accurate positioning under a specific pumping posture. Based on only pressure feedback, the amplitude and the time width of the two impulses are determined via system identification in advance and vibration prediction in real-time. Experimental tests are carried out using a 13m-length hydraulic manipulator under three different postures. The test results show that the vibration caused by disturbance can be effectively reduced using the proposed method, and more importantly the position of the end point can be maintained accurately.
{"title":"Residual vibration suppression of large-size flexible hydraulic manipulator under external disturbance with accurate positioning","authors":"Min Cheng , Xin Zhang , Ruqi Ding , Junhui Zhang , Bing Xu","doi":"10.1016/j.mechatronics.2025.103355","DOIUrl":"10.1016/j.mechatronics.2025.103355","url":null,"abstract":"<div><div>To reduce residual vibration with accurate positioning for a flexible hydraulic manipulator, this paper proposes a dual-impulse vibration suppression method to implement concrete pumping tasks. Through sealing up the load-bearing chamber and allow fluid exchange in the non-bearing chamber by individual metering control (IMC), a valve-based volume control method without position sensors is proposed to replace direct positioning control of the end point. Besides, a dual-impulse valve controller is designed for making an online tradeoff between vibration suppression and accurate positioning under a specific pumping posture. Based on only pressure feedback, the amplitude and the time width of the two impulses are determined via system identification in advance and vibration prediction in real-time. Experimental tests are carried out using a 13m-length hydraulic manipulator under three different postures. The test results show that the vibration caused by disturbance can be effectively reduced using the proposed method, and more importantly the position of the end point can be maintained accurately.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103355"},"PeriodicalIF":3.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213503","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 : 2025-06-04DOI: 10.1016/j.mechatronics.2025.103354
Yunzhi Zhang , Jie Ling , Micky Rakotondrabe , Yuchuan Zhu , Dan Wang
Piezoelectric actuators (PEAs) play a key role in precision engineering, but their strong rate-dependent hysteresis affects accuracy. Existing hysteresis models fail to capture the simultaneous rotation and expansion of hysteresis at high rates. This paper proposes a modified Prandtl–Ishlinskii model in a Hammerstein-like architecture (HAMPI) aiming to model the rotation and expansion of the hysteresis at different input rates. Simulations and experiments are conducted to validate the HAMPI model across a wide range of input rates (50–500 Hz) and amplitudes (0–140 V), revealing that the proposed model has the root-mean-square error (resp. relative root-mean-square error) of 0.47 m (resp. 3.07%), which is lower than the results of existing hysteresis model. Additionally, a HAMPI-based feedforward controller with the inverse multiplicative structure shows that the tracking performance RMS error (resp. NRMS error) can be kept within 0.09 m (resp. 2.25%) when the operating frequency is below 150 Hz. Meanwhile, the displacement attenuation issue in feedforward control caused by the rate-dependent rotation of hysteresis loops is also successfully addressed by the proposed HAMPI model.
{"title":"Modeling and feedforward control of hysteresis in piezoelectric actuators considering its rotation and expansion","authors":"Yunzhi Zhang , Jie Ling , Micky Rakotondrabe , Yuchuan Zhu , Dan Wang","doi":"10.1016/j.mechatronics.2025.103354","DOIUrl":"10.1016/j.mechatronics.2025.103354","url":null,"abstract":"<div><div>Piezoelectric actuators (PEAs) play a key role in precision engineering, but their strong rate-dependent hysteresis affects accuracy. Existing hysteresis models fail to capture the simultaneous rotation and expansion of hysteresis at high rates. This paper proposes a modified Prandtl–Ishlinskii model in a Hammerstein-like architecture (HAMPI) aiming to model the rotation and expansion of the hysteresis at different input rates. Simulations and experiments are conducted to validate the HAMPI model across a wide range of input rates (50–500 Hz) and amplitudes (0–140 V), revealing that the proposed model has the root-mean-square error (resp. relative root-mean-square error) of 0.47 <span><math><mi>μ</mi></math></span>m (resp. 3.07%), which is lower than the results of existing hysteresis model. Additionally, a HAMPI-based feedforward controller with the inverse multiplicative structure shows that the tracking performance RMS error (resp. NRMS error) can be kept within 0.09 <span><math><mi>μ</mi></math></span>m (resp. 2.25%) when the operating frequency is below 150 Hz. Meanwhile, the displacement attenuation issue in feedforward control caused by the rate-dependent rotation of hysteresis loops is also successfully addressed by the proposed HAMPI model.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103354"},"PeriodicalIF":3.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203724","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 : 2025-05-30DOI: 10.1016/j.mechatronics.2025.103341
Oussama Bey, Yacine Amirat, Samer Mohammed
Actuated Ankle-Foot Orthoses (AAFOs) assist dorsiflexion and plantarflexion movements at the ankle joint, supporting mobility and rehabilitation by complementing the wearer’s residual muscular activity within an assist-as-needed paradigm. Their effectiveness depends on advanced control strategies and accurate modeling of the coupled human-AAFO dynamics, which remains a challenging task. This paper presents a novel assist-as-needed control approach for an AAFO/wearer system based on an adaptive model-free framework, without the need for a dynamic model of the AAFO/wearer system. The proposed approach uses an ultra-local model, wherein a intelligent projection-based adaptive PID (iA-PID) controller is designed to achieve satisfactory tracking of a reference ankle joint trajectory. External torques affecting the AAFO/wearer system are estimated using a time-delay estimator and are compensated within the iPA-PID controller to ensure assist-as-needed control. Additionally, the projection operator constrains the evolution of the adaptive parameters, preventing actuator saturation and enabling controlled assistance delivery. Finite-time stability of the resulting closed-loop system is proven, and the final value theorem ensures that the tracking error converges to zero. The performance of the proposed approach is evaluated through simulations and real-time experiments with four healthy subjects. A comparison of tracking performance with several benchmark approaches was conducted as well as robustness tests under varying walking speeds to confirm the effectiveness and reliability of the proposed control approach.
{"title":"Adaptive model-free control for ankle-assistive orthosis: A robust approach to real-time gait tracking","authors":"Oussama Bey, Yacine Amirat, Samer Mohammed","doi":"10.1016/j.mechatronics.2025.103341","DOIUrl":"10.1016/j.mechatronics.2025.103341","url":null,"abstract":"<div><div>Actuated Ankle-Foot Orthoses (AAFOs) assist dorsiflexion and plantarflexion movements at the ankle joint, supporting mobility and rehabilitation by complementing the wearer’s residual muscular activity within an assist-as-needed paradigm. Their effectiveness depends on advanced control strategies and accurate modeling of the coupled human-AAFO dynamics, which remains a challenging task. This paper presents a novel assist-as-needed control approach for an AAFO/wearer system based on an adaptive model-free framework, without the need for a dynamic model of the AAFO/wearer system. The proposed approach uses an ultra-local model, wherein a intelligent projection-based adaptive PID (iA-PID) controller is designed to achieve satisfactory tracking of a reference ankle joint trajectory. External torques affecting the AAFO/wearer system are estimated using a time-delay estimator and are compensated within the iPA-PID controller to ensure assist-as-needed control. Additionally, the projection operator constrains the evolution of the adaptive parameters, preventing actuator saturation and enabling controlled assistance delivery. Finite-time stability of the resulting closed-loop system is proven, and the final value theorem ensures that the tracking error converges to zero. The performance of the proposed approach is evaluated through simulations and real-time experiments with four healthy subjects. A comparison of tracking performance with several benchmark approaches was conducted as well as robustness tests under varying walking speeds to confirm the effectiveness and reliability of the proposed control approach.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"109 ","pages":"Article 103341"},"PeriodicalIF":3.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169443","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 : 2025-05-30DOI: 10.1016/j.mechatronics.2025.103351
Lin Li , Serdar Coskun , Youming Fan , Caiguang Yu , Fengqi Zhang
For lane change behavior under extreme operating conditions, existing models cannot calculate in real time the tire force of the vehicle lane change over a sufficiently long time frame in the future. In order to address this problem, a novel scheme is presented for real-time trajectory planning of autonomous vehicles, which incorporates personalized vehicle dynamics. We first establish lateral dynamics models for four-wheel-steering and front-wheel-steering vehicles along with a nonlinear tire model. Then, we construct a fuzzy logic mechanism to characterize the relationship between the vehicle lateral/longitudinal acceleration and the future lateral/longitudinal tire force, to quantify whether the vehicle tire force reaches saturation in trajectory planning in real time. A safety assessment model is introduced to measure the risk of side slippage of the vehicle and collision under extreme operating conditions. In addition, lane change behavior is designed as a nonlinear programming model and a gradient descent method is used to obtain optimal lateral and longitudinal accelerations online. The geometric curve fitting method is utilized to generate the lane change trajectory. The simulation results using MATLAB/Simulink demonstrate that the solution time of our method is significantly lower than that of the widely used vehicle dynamics method and the newest Neural Network method, which can realize real-time prediction of the maximum tire force before lane change. Moreover, our method improves the ability to calculate the risk of longitudinal and lateral coupling of a lane change in extreme operating conditions and then realizes trajectory planning in a vehicle-dynamics-specific way.
{"title":"A real-time lane change trajectory planning approach for autonomous vehicles utilizing tire force prediction","authors":"Lin Li , Serdar Coskun , Youming Fan , Caiguang Yu , Fengqi Zhang","doi":"10.1016/j.mechatronics.2025.103351","DOIUrl":"10.1016/j.mechatronics.2025.103351","url":null,"abstract":"<div><div>For lane change behavior under extreme operating conditions, existing models cannot calculate in real time the tire force of the vehicle lane change over a sufficiently long time frame in the future. In order to address this problem, a novel scheme is presented for real-time trajectory planning of autonomous vehicles, which incorporates personalized vehicle dynamics. We first establish lateral dynamics models for four-wheel-steering and front-wheel-steering vehicles along with a nonlinear tire model. Then, we construct a fuzzy logic mechanism to characterize the relationship between the vehicle lateral/longitudinal acceleration and the future lateral/longitudinal tire force, to quantify whether the vehicle tire force reaches saturation in trajectory planning in real time. A safety assessment model is introduced to measure the risk of side slippage of the vehicle and collision under extreme operating conditions. In addition, lane change behavior is designed as a nonlinear programming model and a gradient descent method is used to obtain optimal lateral and longitudinal accelerations online. The geometric curve fitting method is utilized to generate the lane change trajectory. The simulation results using MATLAB/Simulink demonstrate that the solution time of our method is significantly lower than that of the widely used vehicle dynamics method and the newest Neural Network method, which can realize real-time prediction of the maximum tire force before lane change. Moreover, our method improves the ability to calculate the risk of longitudinal and lateral coupling of a lane change in extreme operating conditions and then realizes trajectory planning in a vehicle-dynamics-specific way.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"109 ","pages":"Article 103351"},"PeriodicalIF":3.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169454","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 : 2025-05-28DOI: 10.1016/j.mechatronics.2025.103342
Nelson Cisneros, Yongxin Wu, Kanty Rabenorosoa, Yann Le Gorrec
This paper addresses the modeling, parameter identification, and validation of curling Hydraulically Amplified Self-healing Electrostatic (HASEL) actuators using the port Hamiltonian (PH) framework. Employing a modular approach, the HASEL actuator is conceptualized as a combination of elementary subsystems. Each subsystem includes electrical and mechanical components. The electrical component is characterized by a variable capacitor in parallel with a resistor branch, which is in series with another capacitor that is also in parallel with a resistor branch, representing charge retention-related drift. The mechanical component consists of linear and torsional springs connected to an equivalent mass. The parameters of the proposed model were identified using the Levenberg–Marquardt optimization algorithm with data from the developed experimental setup. Additional sets of experimental data were used to validate the obtained model.
{"title":"Dynamic modeling of a curling HASEL actuator using the port Hamiltonian framework with experimental validation","authors":"Nelson Cisneros, Yongxin Wu, Kanty Rabenorosoa, Yann Le Gorrec","doi":"10.1016/j.mechatronics.2025.103342","DOIUrl":"10.1016/j.mechatronics.2025.103342","url":null,"abstract":"<div><div>This paper addresses the modeling, parameter identification, and validation of curling Hydraulically Amplified Self-healing Electrostatic (HASEL) actuators using the port Hamiltonian (PH) framework. Employing a modular approach, the HASEL actuator is conceptualized as a combination of elementary subsystems. Each subsystem includes electrical and mechanical components. The electrical component is characterized by a variable capacitor in parallel with a resistor branch, which is in series with another capacitor that is also in parallel with a resistor branch, representing charge retention-related drift. The mechanical component consists of linear and torsional springs connected to an equivalent mass. The parameters of the proposed model were identified using the Levenberg–Marquardt optimization algorithm with data from the developed experimental setup. Additional sets of experimental data were used to validate the obtained model.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"109 ","pages":"Article 103342"},"PeriodicalIF":3.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147966","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 : 2025-05-27DOI: 10.1016/j.mechatronics.2025.103352
Robbe De Laet , Nick Van Oosterwyck , Annie Cuyt , Stijn Derammelaere
Point-to-point mechanisms are widely used in industry. The electric motors driving these mechanisms are responsible for a significant portion of the global energy consumption. Therefore, it is essential to consider methods and technologies to reduce their energy consumption. Motion profile optimization offers a cost-effective opportunity to reduce energy consumption without the need for additional hardware adaptations or investments. Hence, it is crucial to discover the global optimum, representing the overall best solution across the entire design space, to ensure that the full optimization potential is realized, rather than settling for a local optimum, which may only represent a superior solution within a limited region of the design space. The latter remains a challenge in the current literature. This paper introduces a novel approach that utilizes interval analysis to guarantee the discovery of the global optimum within the design space. It achieves this by dividing the design space into smaller intervals, employing interval arithmetic to evaluate the functions over these intervals, and systematically eliminating and refining the intervals to pinpoint the location of the global optimum. However, to use interval analysis, a bounded design space is required. Therefore, this paper describes the motion profile using polynomials expressed in the Chebyshev basis, offering the advantage of a bounded design space and a minimal number of design parameters compared to polynomials expressed in the classical basis. Additionally, this paper shows that symbolically formulating the motion profile allows linearization of the kinematic constraints, enhancing the computational efficiency and convergence speed of interval analysis. Furthermore, a method for reducing the initial design space is introduced, as the initial bounded design space tends to overestimate the feasible design space. Refining the design space enables interval analysis to require fewer evaluations, facilitating faster optimization. To allow wide industrial adoption of the proposed method, the system properties and are extracted from CAD simulations to build the objective function. Finally, measurements indicate a reduction in root-mean-square (rms) torque of a pick-and-place unit by up to 38.4% and a reduction in energy consumption of up to 51.2%, validating the proposed approach’s effectiveness.
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