Model-Free Feedback Constrained Optimization Via Projected Primal-Dual Zeroth-Order Dynamics
Year: 2022
Type: preprint
Abstract: In this paper, we propose a model-free feedback solution method to solve generic constrained optimization problems, without knowing the specific formulations of the objective and constraint functions. This solution method is termed projected primal-dual zeroth-order dynamics (P-PDZD) and is developed based on projected primal-dual gradient dynamics and extremum seeking control. In particular, the P-PDZD method can be interpreted as a model-free controller that autonomously drives an unknown system to the solution of the optimization problem using only output feedback. The P-PDZD can properly handle both the hard and asymptotic constraints, and we develop the decentralized version of P-PDZD when applied to multi-agent systems. Moreover, we prove that the P-PDZD achieves semi-global practical asymptotic stability and structural robustness. We then apply the decentralized P-PDZD to the optimal voltage control problem in power distribution systems with square probing signals, and the simulation results verified the optimality, robustness, and adaptivity of the P-PDZD method.
Source: arXiv (Cornell University)
Authors: Chen, Xin, Poveda, Jorge I., Li, Na
Institutions: Harvard University, University of Colorado Boulder
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: CAREER: Optimization, Control, and Incentive Design for Power Networks with High Levels of Distributed Energy Resources, CRII: CPS: High-Performance Adaptive Hybrid Feedback Algorithms for Real-Time Optimization and Learning in Networked Transportation Systems, EAGER: Real-Time: Learning, Selection, and Control in Residential Demand Response for Grid Reliability, G6671297155, G7553961619
OpenAlex ID: W4283366350