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Lyapunov-Based Online Optimal Control of a Class of Cascaded Systems with an Infinite Horizon Time-Average Performance Guarantee

Year: 2025

Type: conference-paper

Abstract: In this paper, we investigate the optimal control of a class of cascaded systems, where mass and/or energy flow through subsystems connected in series. In a stochastic setting, model predictive control (MPC) is a widely adopted approach for tackling this problem. However, it can be computationally intensive and, perhaps more importantly, relies on the characterization of uncertainty. To overcome these potential limitations, we propose a Lyapunov-based online optimal control method, which operates in real time without requiring prior characterization of the uncertainty. By relaxing time-coupling constraints, the proposed online method becomes suboptimal compared to offline optimization with perfect foresight, i.e., the ideal solution to the optimal control problem. Our main result establishes an infinite horizon time-average performance guarantee, demonstrating bounded suboptimality of the proposed method. We apply the Lyapunov-based online optimal control method to the economic dispatch of cascaded hydropower plants, and show that it can outperform both greedy online control and popular MPC.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc57313.2025.11312530

Authors: Luca Santosuosso, Panagiotis Andrianesis

Institutions: Graz University of Technology, Université Paris Sciences et Lettres, Centre Procédés, Energies Renouvelables et Systèmes Energétiques, École Nationale Supérieure des Mines de Paris

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4414685071

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