Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
arXiv:2103.13988 · doi:10.1109/CDC45484.2021.9683614
Abstract
This paper proposes a general framework for constructing feedback controllers that drive complex dynamical systems to "efficient" steady-state (or slowly varying) operating points. Efficiency is encoded using generalized equations which can model a broad spectrum of useful objectives, such as optimality or equilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of the proposed approach is to directly implement iterative solution (or equilibrium seeking) algorithms in closed loop with physical systems. Sufficient conditions for closed-loop stability and robustness are derived; these also serve as the first closed-loop stability results for sampled-data feedback-based optimization. Numerical simulations of smart building automation and game-theoretic robotic swarm coordination support the theoretical results.
References in corpus (10)
- A Passivity-Based Approach to Nash Equilibrium Seeking over Networks
- Real and Complex Monotone Communication Games
- Dynamic NE Seeking for Multi-Integrator Networked Agents with Disturbance Rejection
- Online Primal-Dual Methods with Measurement Feedback for Time-Varying Convex Optimization
- Time Distributed Optimization for Model Predictive Control: Stability, Robustness, and Constraint Satisfaction
- Experimental Validation of Feedback Optimization in Power Distribution Grids
- Non-convex Feedback Optimization with Input and Output Constraints
- Optimization Algorithms as Robust Feedback Controllers
- Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
- Time-varying Projected Dynamical Systems with Applications to Feedback Optimization of Power Systems
Cited by in corpus (5)
- Model-Free Nonlinear Feedback Optimization
- Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
- Online Stochastic Optimization for Unknown Linear Systems: Data-Driven Synthesis and Controller Analysis
- Input-to-State Stability of Newton Methods for Generalized Equations in Nonlinear Optimization
- On a closed-loop identification challenge in feedback optimization