TR2024-091
A Switched Reference Governor for High Performance Trajectory Tracking under State and Input Constraints
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- "A Switched Reference Governor for High Performance Trajectory Tracking under State and Input Constraints", American Control Conference (ACC), July 2024.BibTeX TR2024-091 PDF
- @inproceedings{Wang2024jul,
- author = {Wang, Nan and Di Cairano, Stefano and Sanfelice, Ricardo}},
- title = {A Switched Reference Governor for High Performance Trajectory Tracking under State and Input Constraints},
- booktitle = {American Control Conference (ACC)},
- year = 2024,
- month = jul,
- url = {https://www.merl.com/publications/TR2024-091}
- }
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- "A Switched Reference Governor for High Performance Trajectory Tracking under State and Input Constraints", American Control Conference (ACC), July 2024.
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Abstract:
This paper proposes a switched reference governor (RG) algorithm to achieve rapid and non-oscilliatory convergence to a given reference signal while satisfying the imposed constraints by switching between a fast and oscilliatory controller and a slow and non-oscilliatory controller. The switched RG first computes the set of the pairs of state and its admissible references for both controllers in an offline fashion. For the online computation, at each iteration, the proposed algorithm computes the admissible reference sets for each controller at the current state. Then, the algorithm activates one of the controllers based on the closeness between the system state and the reference. At last, a lightweight optimization problem to find the admissible reference that is closest to the reference signal is solved and the solution, which is referred to as virtual reference, is applied to the control system as the reference signal. Through measuring the closeness between the system state and the reference by a Lyapunov function and a discrete-time hybrid system model, we show robust switching, recursive feasibility and convergence of the virtual reference to the reference sig
Related News & Events
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NEWS MERL researchers present 9 papers at ACC 2024 Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.