The spring tube is the core component of the hydraulic servovalve, and its stiffness characteristics determine the sensitivity of the servovalve. For the difficulty in measuring and ensuring the stiffness of spring tube in complex structures, based on the principle of structural characteristics and stiffness measurement, an effective method for measuring and evaluating the stiffness of spring tube was proposed. First, by the force analysis of the spring tube in the valve structure, using improved stiffness measurement theory, an equivalent measurement model of single-arm is established. Second, the stiffness measurement system of the spring tube is constructed based on this model. Furthermore, using the deformation and the spatial position recurrence method, the accuracy of the measurement system is further improved. Thirdly, using the orthogonal test method and linear optimization method of the neural network model, the stiffness characteristics of the spring tube under the influence of different factors are studied further. Finally, the validity of the models is verified by using the software comsol and the experimental platform. The stability of the effective stiffness for the spring tube is further analyzed by the measurement data. The contribution and novelty of this paper are that based on the force analysis of the spring tube in the servovalve internal structure, an effective and systematic stiffness measurement and evaluation method are proposed. On this basis, experiments and stiffness characteristics analysis are carried out. Furthermore, several structural factors affecting the stiffness characteristics of spring tube are considered, and the stiffness characteristics of spring tube are systematically studied and analyzed. Based on this research and analysis, the systematic study of measurement and characteristics of precision components is very important for practical complex systems in this field. This makes it possible to further study the measurement of precision components which are difficult to measure in the actual structure. It is instructive to study the characteristics of precision components in complex structures.
{"title":"Measurement and Evaluation of the Stiffness Characteristics of Precision Spring Tubes in Hydraulic Servovalves","authors":"Fuli Zhang, Zhaohui Yuan","doi":"10.1115/1.4050573","DOIUrl":"https://doi.org/10.1115/1.4050573","url":null,"abstract":"\u0000 The spring tube is the core component of the hydraulic servovalve, and its stiffness characteristics determine the sensitivity of the servovalve. For the difficulty in measuring and ensuring the stiffness of spring tube in complex structures, based on the principle of structural characteristics and stiffness measurement, an effective method for measuring and evaluating the stiffness of spring tube was proposed. First, by the force analysis of the spring tube in the valve structure, using improved stiffness measurement theory, an equivalent measurement model of single-arm is established. Second, the stiffness measurement system of the spring tube is constructed based on this model. Furthermore, using the deformation and the spatial position recurrence method, the accuracy of the measurement system is further improved. Thirdly, using the orthogonal test method and linear optimization method of the neural network model, the stiffness characteristics of the spring tube under the influence of different factors are studied further. Finally, the validity of the models is verified by using the software comsol and the experimental platform. The stability of the effective stiffness for the spring tube is further analyzed by the measurement data. The contribution and novelty of this paper are that based on the force analysis of the spring tube in the servovalve internal structure, an effective and systematic stiffness measurement and evaluation method are proposed. On this basis, experiments and stiffness characteristics analysis are carried out. Furthermore, several structural factors affecting the stiffness characteristics of spring tube are considered, and the stiffness characteristics of spring tube are systematically studied and analyzed. Based on this research and analysis, the systematic study of measurement and characteristics of precision components is very important for practical complex systems in this field. This makes it possible to further study the measurement of precision components which are difficult to measure in the actual structure. It is instructive to study the characteristics of precision components in complex structures.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"72 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86154980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion","authors":"Yong Ren, Qian Wang, P. Michaleris","doi":"10.1115/1.4052245","DOIUrl":"https://doi.org/10.1115/1.4052245","url":null,"abstract":"","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"180 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74376780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inversion-based Hysteresis Compensation Using Adaptive Conditional Servocompensator for Nanopositioning Systems","authors":"Y. K. Al-Nadawi, Xiaobo Tan, H. Khalil","doi":"10.1115/1.4052229","DOIUrl":"https://doi.org/10.1115/1.4052229","url":null,"abstract":"","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88696893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A warm start method is developed for efficiently solving complex chance constrained optimal control problems using biased kernel density estimators and Legendre–Gauss–Radau collocation. To address the computational challenges, the warm start method improves both the starting point for the chance constrained optimal control problem, as well as the efficiency of cycling through mesh refinement iterations. The improvement is accomplished by tuning a parameter of the kernel density estimator, as well as implementing a kernel switch as part of the solution process. Additionally, the number of samples for the biased kernel density estimator is set to incrementally increase through a series of mesh refinement iterations. Thus, the warm start method is a combination of tuning a parameter, a kernel switch, and an incremental increase in sample size. This warm start method is successfully applied to solve two challenging chance constrained optimal control problems in a computationally efficient manner using biased kernel density estimators and Legendre–Gauss–Radau collocation. [DOI: 10.1115/1.4052173]
{"title":"Warm Start Method for Solving Chance Constrained Optimal Control Problems Using Biased Kernel Density Estimators","authors":"Rachel E. Keil, Mrinal Kumar, Anil V. Rao","doi":"10.1115/1.4052173","DOIUrl":"https://doi.org/10.1115/1.4052173","url":null,"abstract":"A warm start method is developed for efficiently solving complex chance constrained optimal control problems using biased kernel density estimators and Legendre–Gauss–Radau collocation. To address the computational challenges, the warm start method improves both the starting point for the chance constrained optimal control problem, as well as the efficiency of cycling through mesh refinement iterations. The improvement is accomplished by tuning a parameter of the kernel density estimator, as well as implementing a kernel switch as part of the solution process. Additionally, the number of samples for the biased kernel density estimator is set to incrementally increase through a series of mesh refinement iterations. Thus, the warm start method is a combination of tuning a parameter, a kernel switch, and an incremental increase in sample size. This warm start method is successfully applied to solve two challenging chance constrained optimal control problems in a computationally efficient manner using biased kernel density estimators and Legendre–Gauss–Radau collocation. [DOI: 10.1115/1.4052173]","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"50 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84451433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Observer-Based Stabilization of Linear Discrete Time-Varying Delay Systems","authors":"Venkatesh Modala, S. Patra, G. Ray","doi":"10.1115/1.4052041","DOIUrl":"https://doi.org/10.1115/1.4052041","url":null,"abstract":"","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"100 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75457655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Plant-Input-Mapping Discretization Method for a Feedback System in the State-Space Form","authors":"Keisuke Yagi, Hiroaki Muto, Y. Mori","doi":"10.1115/1.4052040","DOIUrl":"https://doi.org/10.1115/1.4052040","url":null,"abstract":"","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89667828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Myoungho Kim, Joohwan Seo, Mingoo Lee, Jongeun Choi
Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.
{"title":"Vision-Based Uncertainty-Aware Lane Keeping Strategy Using Deep Reinforcement Learning","authors":"Myoungho Kim, Joohwan Seo, Mingoo Lee, Jongeun Choi","doi":"10.1115/1.4050396","DOIUrl":"https://doi.org/10.1115/1.4050396","url":null,"abstract":"\u0000 Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"127 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80152425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study uses a low-density solid-state flash lidar for estimating the trajectories of road vehicles in vehicle collision avoidance applications. Low-density flash lidars are inexpensive compared to the commonly used radars and point-cloud lidars, and have attracted the attention of vehicle manufacturers recently. However, tracking road vehicles using the sparse data provided by such sensors is challenging due to the few reflected measurement points obtained. In this paper, such challenges in the use of low-density flash lidars are identified and estimation algorithms to handle the same are presented. A method to use the amplitude information provided by the sensor for better localization of targets is evaluated using both physics-based simulations and experiments. A two-step hierarchical clustering algorithm is then employed to group multiple detections from a single object into one measurement, which is then associated with the corresponding object using a Joint Integrated Probabilistic Data Association (JIPDA) algorithm. A Kalman filter is used to estimate the longitudinal and lateral motion variables and the results are presented, which show that good tracking, especially in the lateral direction, can be achieved using the proposed algorithm despite the sparse measurements provided by the sensor.
{"title":"On Using a Low-Density Flash Lidar for Road Vehicle Tracking","authors":"Vimal Kumar A. R., S. Subramanian, R. Rajamani","doi":"10.1115/1.4050255","DOIUrl":"https://doi.org/10.1115/1.4050255","url":null,"abstract":"\u0000 This study uses a low-density solid-state flash lidar for estimating the trajectories of road vehicles in vehicle collision avoidance applications. Low-density flash lidars are inexpensive compared to the commonly used radars and point-cloud lidars, and have attracted the attention of vehicle manufacturers recently. However, tracking road vehicles using the sparse data provided by such sensors is challenging due to the few reflected measurement points obtained. In this paper, such challenges in the use of low-density flash lidars are identified and estimation algorithms to handle the same are presented. A method to use the amplitude information provided by the sensor for better localization of targets is evaluated using both physics-based simulations and experiments. A two-step hierarchical clustering algorithm is then employed to group multiple detections from a single object into one measurement, which is then associated with the corresponding object using a Joint Integrated Probabilistic Data Association (JIPDA) algorithm. A Kalman filter is used to estimate the longitudinal and lateral motion variables and the results are presented, which show that good tracking, especially in the lateral direction, can be achieved using the proposed algorithm despite the sparse measurements provided by the sensor.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"12 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72440516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces an adaptive robust trajectory tracking controller design to provably realize stable bipedal robotic walking under parametric and unmodeled uncertainties. Deriving such a controller is challenging mainly because of the highly complex bipedal walking dynamics that are hybrid and involve nonlinear, uncontrolled state-triggered jumps. The main contribution of the study is the synthesis of a continuous-phase adaptive robust tracking control law for hybrid models of bipedal robotic walking by incorporating the construction of multiple Lyapunov functions into the control Lyapunov function. The evolution of the Lyapunov function across the state-triggered jumps is explicitly analyzed to construct sufficient conditions that guide the proposed control design for provably guaranteeing the stability and tracking the performance of the hybrid system in the presence of uncertainties. Simulation results on fully actuated bipedal robotic walking validate the effectiveness of the proposed approach in walking stabilization under uncertainties.
{"title":"Adaptive Robust Tracking Control for Hybrid Models of Three-Dimensional Bipedal Robotic Walking Under Uncertainties","authors":"Yan Gu, C. Yuan","doi":"10.1115/1.4050259","DOIUrl":"https://doi.org/10.1115/1.4050259","url":null,"abstract":"\u0000 This paper introduces an adaptive robust trajectory tracking controller design to provably realize stable bipedal robotic walking under parametric and unmodeled uncertainties. Deriving such a controller is challenging mainly because of the highly complex bipedal walking dynamics that are hybrid and involve nonlinear, uncontrolled state-triggered jumps. The main contribution of the study is the synthesis of a continuous-phase adaptive robust tracking control law for hybrid models of bipedal robotic walking by incorporating the construction of multiple Lyapunov functions into the control Lyapunov function. The evolution of the Lyapunov function across the state-triggered jumps is explicitly analyzed to construct sufficient conditions that guide the proposed control design for provably guaranteeing the stability and tracking the performance of the hybrid system in the presence of uncertainties. Simulation results on fully actuated bipedal robotic walking validate the effectiveness of the proposed approach in walking stabilization under uncertainties.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"7 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87920246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Costa, G. Palmieri, D. Scaradozzi, M. Callegari
Bio-inspired solutions have been deeply investigated in the last two decades as a source of propulsive improvement for autonomous underwater vehicles. Despite the efforts made to pursue the substantial potential payoffs of marine animals' locomotion, the performance of biological swimmers is still far to reach. The possibility to design a machine capable of propelling itself like a marine animal strongly depends on the understanding of the mechanics principles underlying biological swimming. Therefore, the adoption of advanced simulation and measurement techniques is fundamental to investigate the fluid–structure interaction phenomena of aquatic animals' locomotion. Among those, computational fluid dynamics represents an invaluable tool to assess the propulsive loads due to swimming. However, the numerical predictions must be validated before they can be applied to the design of a bio-inspired robot. To this end, this paper presents the experimental setup devised to validate the fluid dynamics analysis performed on an oscillating foil. The numerical predictions led to the design of a strain gages-based sensor, which exploits the deflection and twisting of the foil shaft to indirectly measure the propulsive loads and obtain a complete dynamic characterization of the oscillating foil. The results obtained from the experiments showed a good agreement between the numerical predictions and the measured loads; the test equipment also allowed to investigate the potential benefits of a slender fish-like body placed before the spinning fin. Therefore, in future work, the system will be employed to validate the analysis performed on more sophisticated modes of locomotion.
{"title":"Experimental Validation of a Bio-Inspired Thruster","authors":"D. Costa, G. Palmieri, D. Scaradozzi, M. Callegari","doi":"10.1115/1.4050258","DOIUrl":"https://doi.org/10.1115/1.4050258","url":null,"abstract":"\u0000 Bio-inspired solutions have been deeply investigated in the last two decades as a source of propulsive improvement for autonomous underwater vehicles. Despite the efforts made to pursue the substantial potential payoffs of marine animals' locomotion, the performance of biological swimmers is still far to reach. The possibility to design a machine capable of propelling itself like a marine animal strongly depends on the understanding of the mechanics principles underlying biological swimming. Therefore, the adoption of advanced simulation and measurement techniques is fundamental to investigate the fluid–structure interaction phenomena of aquatic animals' locomotion. Among those, computational fluid dynamics represents an invaluable tool to assess the propulsive loads due to swimming. However, the numerical predictions must be validated before they can be applied to the design of a bio-inspired robot. To this end, this paper presents the experimental setup devised to validate the fluid dynamics analysis performed on an oscillating foil. The numerical predictions led to the design of a strain gages-based sensor, which exploits the deflection and twisting of the foil shaft to indirectly measure the propulsive loads and obtain a complete dynamic characterization of the oscillating foil. The results obtained from the experiments showed a good agreement between the numerical predictions and the measured loads; the test equipment also allowed to investigate the potential benefits of a slender fish-like body placed before the spinning fin. Therefore, in future work, the system will be employed to validate the analysis performed on more sophisticated modes of locomotion.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"21 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74162553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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