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1دورية أكاديمية
المؤلفون: Jun-ichi IMURA, Ravi GONDHALEKAR
المصدر: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. 2008, E91.A(11):3253
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2دورية أكاديمية
المؤلفون: Jun-ichi IMURA, Ravi GONDHALEKAR
المصدر: Transactions of the Society of Instrument and Control Engineers. 2007, 43(10):883
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المؤلفون: Charles E. Oestreich, Richard Linares, Ravi Gondhalekar
المصدر: Journal of Guidance, Control, and Dynamics. 46:6-20
مصطلحات موضوعية: Space and Planetary Science, Control and Systems Engineering, Applied Mathematics, Aerospace Engineering, Electrical and Electronic Engineering
الوصف: Autonomous spacecraft must be robust toward uncertainty and disturbances in the system while achieving required levels of performance. As an example, robotic servicing spacecraft must intercept target objects with potentially unknown dynamic properties during active debris removal efforts. This work presents tube-based model predictive control (MPC) with uncertainty identification as a strategy to enhance performance while maintaining robustness in autonomous maneuvers. The proposed algorithm, which is an extension of the standard tube-based MPC framework, measures and predicts the exogenous input to the system (i.e., the uncertainty) online. This in turn enables the robust tube to be shrunk and grown appropriately as the trajectory progresses in both predicted time and actual time. The algorithm is demonstrated in a simulated intercept maneuver with a tumbling target whose inertia tensor is uncertain. Results indicate two improvements over the standard tube-based algorithm: first, better performance is obtained when the initial exogenous input bounds are overconservative, and, second, there is greater flexibility in encouraging robustness when the exogenous input bounds are overly optimistic since the robust tube is updated online. As such, tube-based MPC with uncertainty identification represents an incremental step in enhancing the flexibility of autonomous spacecraft in addressing uncertain scenarios and environments.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::ccf6340312e9b768bf35f59d8d4c9725Test
https://doi.org/10.2514/1.g006438Test -
4Autonomous Six-Degree-of-Freedom Spacecraft Docking with Rotating Targets via Reinforcement Learning
المؤلفون: Charles E. Oestreich, Ravi Gondhalekar, Richard Linares
المصدر: Journal of Aerospace Information Systems. :1-12
مصطلحات موضوعية: 020301 aerospace & aeronautics, 0209 industrial biotechnology, Spacecraft, Computer science, business.industry, Feedback control, Aerospace Engineering, Control engineering, 02 engineering and technology, Linear-quadratic regulator, Trajectory optimization, Computer Science Applications, Three degrees of freedom, 020901 industrial engineering & automation, Docking (dog), 0203 mechanical engineering, Reinforcement learning, Electrical and Electronic Engineering, business
الوصف: A policy for six-degree-of-freedom docking maneuvers with rotating targets is developed through reinforcement learning and implemented as a feedback control law. Potential clients for satellite ser...
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::4b925259c419bdefd48661708c12c03bTest
https://doi.org/10.2514/1.i010914Test -
5دورية أكاديمية
المؤلفون: Ravi Gondhalekar
المساهمون: The Pennsylvania State University CiteSeerX Archives
وصف الملف: application/pdf
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6دورية أكاديمية
المؤلفون: Milan Korda, Ravi Gondhalekar, Frauke Oldewurtel, Colin N. Jones
المساهمون: The Pennsylvania State University CiteSeerX Archives
الوصف: This paper considers linear discrete-time systems with additive, bounded, disturbances subject to hard control input bounds and a stochastic constraint on the amount of state-constraint violation averaged over time. The amount of violations is quantified by a loss function and the averaging can be weighted, corresponding to exponential forgetting of past violations. The freedom in the choice of the loss function makes this formulation highly flexible – for instance, probabilistic constraints or integrated chance constraints can be enforced by an appropriate choice of the loss function. For the type of constraint considered, we develop a recursively feasible receding horizon control scheme exploiting the averaged-over-time nature by explicitly taking into account the amount of past constraint violations when determining the current control input. This leads to a significant reduction in conservatism. As a simple extension of the proposed approach we show how time-varying state-constraints can be handled within our framework. The computational complexity (online as well as offline) is comparable to existing model predictive control schemes. The effectiveness of the proposed methodology is demonstrated by means of a numerical example. 1
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المؤلفون: Eyal Dassau, Francis J. Doyle, Ravi Gondhalekar
المصدر: Automatica. 91:105-117
مصطلحات موضوعية: 0209 industrial biotechnology, Computer science, Drug administration, 030209 endocrinology & metabolism, 02 engineering and technology, Hypoglycemia, medicine.disease, Artificial pancreas, Article, Weighting, Nonlinear programming, Clinical trial, Nonlinear optimization problem, 03 medical and health sciences, Model predictive control, 020901 industrial engineering & automation, 0302 clinical medicine, immune system diseases, Control and Systems Engineering, Control theory, medicine, Electrical and Electronic Engineering
الوصف: A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors’ previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::766a50cdf80475df43b313c08a4ee4a8Test
https://doi.org/10.1016/j.automatica.2018.01.025Test -
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المؤلفون: David A. Copp, Ravi Gondhalekar, Joao P. Hespanha
المصدر: Optimal Control Applications and Methods. 39:904-918
مصطلحات موضوعية: Moving horizon estimation, 0209 industrial biotechnology, Type 1 diabetes, Control and Optimization, business.industry, Applied Mathematics, 030209 endocrinology & metabolism, 02 engineering and technology, medicine.disease, 03 medical and health sciences, Model predictive control, 020901 industrial engineering & automation, 0302 clinical medicine, Control and Systems Engineering, Control theory, Medicine, Blood sugar regulation, business, Software
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::a26ca2e98a1357fb8de3bcd29fe1aa98Test
https://doi.org/10.1002/oca.2388Test -
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المؤلفون: Ananda Basu, Michele Schiavon, Tyler Jean, Steve Patek, Alejandro J. Laguna Sanz, Mei Mei Church, Jordan E. Pinsker, Boris Kovatchev, Ling Hinshaw, Vikash Dadlani, Stacey M. Anderson, Francis J. Doyle, Wendy C. Bevier, Shelly K. McCrady-Spitzer, Elaine Schertz, Rickey E. Carter, Yogish C. Kudva, Dayu Lv, Paige K. Bradley, Emma Emory, Claudio Cobelli, Chiara Dalla Man, Ravi Gondhalekar, Jonathan Hughes, Eyal Dassau, Isuru Dasanayake, Lauren M. Huyett, Sue A. Brown
المصدر: Diabetes Care. 40:1719-1726
مصطلحات موضوعية: Advanced and Specialized Nursing, Type 1 diabetes, medicine.medical_specialty, business.industry, Endocrinology, Diabetes and Metabolism, 030209 endocrinology & metabolism, Hypoglycemia, medicine.disease, Artificial pancreas, Surgery, Clinical trial, 03 medical and health sciences, 0302 clinical medicine, Hemoglobin A, Diabetes mellitus, Internal medicine, Ambulatory, Internal Medicine, Clinical endpoint, Medicine, 030212 general & internal medicine, business
الوصف: OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67b3d9cf252d5f86ad1e074bb4dcb9d9Test
https://doi.org/10.2337/dc17-1188Test -
10دورية أكاديمية
المؤلفون: Frauke Oldewurtel, Ravi Gondhalekar, Colin N. Jones, Manfred Morari
المساهمون: The Pennsylvania State University CiteSeerX Archives
مصطلحات موضوعية: Robust control, Model predictive control, Affine disturbance feedback, Controlled
الوصف: ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
وصف الملف: application/pdf
