Pub Date : 2011-12-01DOI: 10.1109/CDC.2011.6160848
Robert D Gregg
This paper presents a formulation of controlled geometric reduction with one degree of underactuation for mechanical systems with an unactuated cyclic variable subject to passive damping. We show that the first control term in the fully actuated case reduces to passive joint-velocity feedback, which can be equivalently provided by viscous friction. The underactuated control strategy is applied to a five-link 3D biped with a hip, torso, knees, and unactuated yaw at the foot contact point. We show asymptotically stable walking in the presence of passive yawing for realistic friction coefficients.
{"title":"Controlled Reduction of a Five-Link 3D Biped with Unactuated Yaw.","authors":"Robert D Gregg","doi":"10.1109/CDC.2011.6160848","DOIUrl":"https://doi.org/10.1109/CDC.2011.6160848","url":null,"abstract":"<p><p>This paper presents a formulation of controlled geometric reduction with one degree of underactuation for mechanical systems with an unactuated cyclic variable subject to passive damping. We show that the first control term in the fully actuated case reduces to passive joint-velocity feedback, which can be equivalently provided by viscous friction. The underactuated control strategy is applied to a five-link 3D biped with a hip, torso, knees, and unactuated yaw at the foot contact point. We show asymptotically stable walking in the presence of passive yawing for realistic friction coefficients.</p>","PeriodicalId":74517,"journal":{"name":"Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control","volume":"2011 ","pages":"669-674"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CDC.2011.6160848","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33369963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-02-22DOI: 10.1109/CDC.2010.5717296
Naresh N Nandola, Daniel E Rivera
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character.
{"title":"Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions.","authors":"Naresh N Nandola, Daniel E Rivera","doi":"10.1109/CDC.2010.5717296","DOIUrl":"https://doi.org/10.1109/CDC.2010.5717296","url":null,"abstract":"<p><p>This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character.</p>","PeriodicalId":74517,"journal":{"name":"Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control","volume":"2010 ","pages":"6113-6118"},"PeriodicalIF":0.0,"publicationDate":"2011-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CDC.2010.5717296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30104999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-01-01DOI: 10.1109/CDC.2011.6161453
Peter Karasev, Ivan Kolesov, Karol Chudy, Grant Muller, John Xerogeanes, Allen Tannenbaum
Partitioning Magnetic-Resonance-Imaging (MRI) data into salient anatomic structures is a problem in medical imaging that has continued to elude fully automated solutions. Implicit functions are a common way to model the boundaries between structures and are amenable to control-theoretic methods. In this paper, the goal of enabling a human to obtain accurate segmentations in a short amount of time and with little effort is transformed into a control synthesis problem. Perturbing the state and dynamics of an implicit function's driving partial differential equation via the accumulated user inputs and an observer-like system leads to desirable closed-loop behavior. Using a Lyapunov control design, a balance is established between the influence of a data-driven gradient flow and the human's input over time. Automatic segmentation is thus smoothly coupled with interactivity. An application of the mathematical methods to orthopedic segmentation is shown, demonstrating the expected transient and steady state behavior of the implicit segmentation function and auxiliary observer.
{"title":"Interactive MRI Segmentation with Controlled Active Vision.","authors":"Peter Karasev, Ivan Kolesov, Karol Chudy, Grant Muller, John Xerogeanes, Allen Tannenbaum","doi":"10.1109/CDC.2011.6161453","DOIUrl":"https://doi.org/10.1109/CDC.2011.6161453","url":null,"abstract":"<p><p>Partitioning Magnetic-Resonance-Imaging (MRI) data into salient anatomic structures is a problem in medical imaging that has continued to elude fully automated solutions. Implicit functions are a common way to model the boundaries between structures and are amenable to control-theoretic methods. In this paper, the goal of enabling a human to obtain accurate segmentations in a short amount of time and with little effort is transformed into a control synthesis problem. Perturbing the state and dynamics of an implicit function's driving partial differential equation via the accumulated user inputs and an observer-like system leads to desirable closed-loop behavior. Using a Lyapunov control design, a balance is established between the influence of a data-driven gradient flow and the human's input over time. Automatic segmentation is thus smoothly coupled with interactivity. An application of the mathematical methods to orthopedic segmentation is shown, demonstrating the expected transient and steady state behavior of the implicit segmentation function and auxiliary observer.</p>","PeriodicalId":74517,"journal":{"name":"Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control","volume":" ","pages":"2293-2298"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CDC.2011.6161453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32165599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号