An integral quadratic constraint framework for real-time steady-state optimization of linear time-invariant systems
It provides a systematic design method for real-time steady-state optimization in critical infrastructure systems, addressing a practical need for optimal performance under disturbances.
This paper proposes a feedback control framework for linear time-invariant systems that continuously tracks the optimal solution of a predefined optimization problem in real-time, guaranteeing optimal steady-state performance even under constant disturbances. The framework uses integral quadratic constraints to derive linear matrix inequality conditions for global exponential stability.
Achieving optimal steady-state performance in real-time is an increasingly necessary requirement of many critical infrastructure systems. In pursuit of this goal, this paper builds a systematic design framework of feedback controllers for Linear Time-Invariant (LTI) systems that continuously track the optimal solution of some predefined optimization problem. The proposed solution can be logically divided into three components. The first component estimates the system state from the output measurements. The second component uses the estimated state and computes a drift direction based on an optimization algorithm. The third component computes an input to the LTI system that aims to drive the system toward the optimal steady-state. We analyze the equilibrium characteristics of the closed-loop system and provide conditions for optimality and stability. Our analysis shows that the proposed solution guarantees optimal steady-state performance, even in the presence of constant disturbances. Furthermore, by leveraging recent results on the analysis of optimization algorithms using integral quadratic constraints (IQCs), the proposed framework is able to translate input-output properties of our optimization component into sufficient conditions, based on linear matrix inequalities (LMIs), for global exponential asymptotic stability of the closed loop system. We illustrate the versatility of our framework using several examples.