A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization: Preprint
- California Institute of Technology
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
This paper considers time-varying nonconvex optimization problems, utilized to model optimal operational trajectories of systems governed by possibly nonlinear physical or logical models. Algorithms for tracking a Karush-Kuhn-Tucker point are synthesized, based on a regularized primal-dual gradient method. In particular, the paper proposes a feedback-based primal-dual gradient algorithm, where analytical models for system state or constraints are replaced with actual measurements. When cost and constraints functions are twice continuously differentiable, conditions for the proposed algorithms to have bounded tracking error are derived, and a discussion of their practical implications is provided. Illustrative numerical simulations are presented for an application in power systems.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- U.S. Department of Energy Advanced Research Projects Agency-Energy (ARPA-E)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1501651
- Report Number(s):
- NREL/CP-5D00-73424
- Resource Relation:
- Conference: Presented at the 2018 IEEE Conference on Decision and Control (CDC), 17-19 December 2018, Miami Beach, Florida
- Country of Publication:
- United States
- Language:
- English
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