
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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Initial Amendment Date: | July 22, 2014 |
Latest Amendment Date: | July 22, 2014 |
Award Number: | 1436774 |
Award Instrument: | Standard Grant |
Program Manager: |
Irina Dolinskaya
idolinsk@nsf.gov (703)292-7078 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | September 1, 2014 |
End Date: | August 31, 2018 (Estimated) |
Total Intended Award Amount: | $160,928.00 |
Total Awarded Amount to Date: | $160,928.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
202 HIMES HALL BATON ROUGE LA US 70803-0001 (225)578-2760 |
Sponsor Congressional District: |
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Primary Place of Performance: |
303 Lockett Hall Baton Rouge LA US 70803-4918 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Dynamics, Control and System D |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
Connecting multiple actuators, controllers, and sensors over shared data networks is a common means of reducing cost and increasing maintainability in modern industrial applications, including automobiles, aircraft, and manufacturing facilities. In most of these applications precise timing is necessary for proper system function, and timing deviations have the potential to cause detrimental and even life-threatening deterioration of performance. However it is inherent to shared networks that contention may occur, meaning that more than one connected device wants to transmit data over the network at the same time. This project will develop real-time networked controllers that resolve contention while achieving desired control objectives. Furthermore, they will be robust to perturbations of the physical system and the network itself. The focus of the project is on network architectures that are common in industry, and the results will apply particularly to automotive control and robotic applications. As reflected in the expertise of the PIs, the project combines insightful engineering with sophisticated mathematics, towards the goal of producing practically useful controllers that have rigorous performance guarantees. Through a series of outreach activities, the project will help broaden participation of underrepresented groups in STEM research.
This project will address among the most challenging and important networked systems problems. It will entail fundamental research to overcome current limitations of model-based control of industrial networks. The project will use a new robust model predictive control framework and event-triggered timing model that combines the strengths of autonomous control and optimization. The work will develop an event-triggered timing model for receding horizon model predictive control of a real-time network, that will handle task dependency and timing variations and adaptively compensate for contentions and time delays. This will allow multiple sensor and actuator nodes for each control loop, a necessity for state-of-the-art networked industrial applications. The controller will respect state and input constraints, optimize cost criteria, predict timing variations, and ensure robustness to perturbations. It will provide least-conservative estimates of robust positive invariant sets in the workspace, and overcome the conservativeness of the best existing results, where the state space is usually chosen to be a sublevel set of a Lyapunov function whose boundary is determined by the supremum of the perturbations. Instead, the controller will seek maximal perturbation bounds that can be allowed before state constraints are violated. Much of the specific implementation, as well as the experimental validation, will emphasize CANbus networks. Because CANbus is popular for real-time industrial control applications, and is the standard protocol for the automotive industry, this will maximize the immediate impact of the results.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project provided state-of-the art methods in systems and controls, which is an interdisciplinary area that yields methods for improving the performance and safety of autonomous systems. By combining engineering principles and developing new mathematical techniques, the project led to innovations in the areas of forward invariance and model predictive control, which can be used to help autonomous systems avoid collisions with obstacles in, or boundaries of, their work spaces, while also providing optimizing solutions to complex control problems that involve multiple agents who must share a common resource. The work included the simultaneous computation of optimizing controls and priority assignments. The techniques from the project were applied to networked systems and models for traffic at an intersection, and were also used to develop curve tracking controls that can help a robot track a desired path and to study the effects of mouse acceleration in human-computer interactions. The broader impacts of the project included the training of PhD students who were co-advised by an engineering professor and a mathematics professor, which enhanced the students' preparation for a broader array of potential careers, and the broad dissemination of the findings in prestigious journals and in the proceedings of premium engineering conferences.
Last Modified: 11/05/2018
Modified by: Michael A Malisoff
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