A Nonlinear Predictive Control Approach for Urban Drainage Networks Using Data-Driven Models and Moving Horizon Estimation
| Autor: | Krisztian Mark Balla, Christian Schou, Jan Dimon Bendtsen, Carlos Ocampo-Martinez, Carsten Skovmose Kallesoe |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Informàtica::Automàtica i control [Àrees temàtiques de la UPC]
receding horizon control Control en temps real urban drainage network (UDN) Partial differential equation (PDE) SDG 13 - Climate Action Electrical and Electronic Engineering graph network Real-time control large-scale systems structure-preserving modeling System Identification Partial Differential Equations multiple shooting transport delay State Estimation SDG 11 - Sustainable Cities and Communities Real Time Control Moving horizon estimation Periodic disturbances Transport delays Predictive Control data-driven modeling Storm water retention basins Control and Systems Engineering high-fidelity model Dipòsits d'aigües pluvials optimization Urban drainage networks nonlinear MPC |
| Zdroj: | Balla, K M, Schou, C, Bendtsen, J D, Ocampo-Martinez, C & Kallesøe, C 2022, ' A Nonlinear Predictive Control Approach for Urban Drainage Networks Using Data-Driven Models and Moving Horizon Estimation ', I E E E Transactions on Control Systems Technology, vol. 30, no. 5, pp. 2147-2162 . https://doi.org/10.1109/TCST.2021.3137712 |
| DOI: | 10.1109/TCST.2021.3137712 |
| Popis: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Real-time control (RTC) of urban drainage networks (UDNs) is a complex task where transport flows are non-pressurized and therefore impose flow-dependent time delays in the system. Unfortunately, the installation of flow sensors is economically out of reach at most utilities, although knowing volumes and flows are essential to optimize system operation. In this article, we formulate joint parameter and state estimation based on level sensors deployed inside manholes and basins in the network. We describe the flow dynamics on the main pipelines by the level variations inside manholes, characterized by a system of coupled partial differential equations (PDEs). These dynamics are approximated with kinematic waves where the network model is established with the water levels being the system states. Moving horizon estimation (MHE) is developed where the states and parameters are obtained via the levels and estimated flow data, utilizing the topological layout of the network. The obtained model complexity is kept within practically achievable limits, suitable for nonlinear predictive control. The effectiveness of the control and estimation method is demonstrated on a high-fidelity model of a drainage network, acting as virtual reality. We use real rain and wastewater flow data and test the controller against the uncertainty in the disturbance forecasts. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|