Award Abstract # 1436774
Collaborative Research: Robustness of Networked Model Predictive Control Satisfying Critical Timing Constraints

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: LOUISIANA STATE UNIVERSITY
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: FY 2014 = $160,928.00
History of Investigator:
  • Michael Malisoff (Principal Investigator)
    malisoff@lsu.edu
Recipient Sponsored Research Office: Louisiana State University
202 HIMES HALL
BATON ROUGE
LA  US  70803-0001
(225)578-2760
Sponsor Congressional District: 06
Primary Place of Performance: Louisiana State Univ. & Ag. & Mech. Coll.
303 Lockett Hall
Baton Rouge
LA  US  70803-4918
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): ECQEYCHRNKJ4
Parent UEI:
NSF Program(s): Dynamics, Control and System D
Primary Program Source: 01001415DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 030E, 031E, 034E, 9102, 9150
Program Element Code(s): 756900
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

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Michael Malisoff and Fumin Zhang "Robustness of Adaptive Control under Time Delays for Three-Dimensional CurveTracking." SIAM Journal on Control and Optimization , v.53 , 2015 , p.2221 10.1137/S0363012903422333
Michael Malisoff, Robert Sizemore, and Fumin Zhang "Adaptive planar curve tracking control and robustness analysis under state constraints and unknown curvature" Automatica , v.75 , 2017 10.1016/j.automatica.2016.09.017
Michael Malisoff, Robert Sizemore, and Fumin Zhang "Robustness of adaptive control for three-dimensional curve tracking under state constraints: effects of scaling control terms" 55th IEEE Conference on Decision and Control. Las Vegas, NV. , 2016 https://doi.org/10.1109/CDC.2016.7798846
Ningshi Yao and Fumin Zhang "Resolving contentions for intelligent traffic intersections using optimal priority assignment and model predictive control" Proceedings of the 2018 IEEE Conference on Control Technology and Applications , 2018
Ningshi Yao, Fumin Zhang, and Michael Malisoff "Priority-based contention resolving scheduling strategies for event-triggered model predictive controllers" 2017 American Control Conference , 2017 10.23919/ACC.2017.7963305
Paul Varnell and Fumin Zhang "Computing largest tolerable disturbance sets" Proceedings of the American Control Conference (Milwaukee, WI, 27-29 June 2018), , 2018
Xiaotian Wang, Zhenwu Shi, Fumin Zhang, and Yue Wang "Dynamic real-time scheduling for human-agentcollaboration systems based on mutual trust" Cyber-Physical Systems , v.1 , 2015 10.1080/23335777.2015.1056755
Zhenwu Shi and Fumin Zhang "Model Predictive Control under Timing Constraints induced by Controller Area Networks" Real-Time Systems , v.53 , 2017 10.1007/s11241-016-9263-2

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

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page