TY - JOUR
T1 - A Tree-Based Multi-Scenario Approach to Networked MPC under Packet Losses and Disturbances
AU - Arauz, T.
AU - Maestre, J. M.
AU - Cetinkaya, A.
AU - Stoica Maniu, C.
N1 - Funding Information:
This project has received funding from the Spanish Training Program for Academic Staff (FPU19/00127), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 789051)-OCONTSOLAR, the project GESVIP funded by Junta de Andalucía (ref. US-1265917) and MCIN/AEI/ 10.13039/501100011033 under project C3PO-R2D2 (PID2020-119476RB-I00). Also, support from the JST ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603) and JSPS Kakenhi Grant (No. JP20K14771) are acknowledged.
Publisher Copyright:
Copyright © 2022 The Authors.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Systems with elements linked via a communication network are vulnerable to communication problems and attacks of malicious agents, with potentially harmful consequences for performance and stability. This paper proposes a stochastic Model Predictive Control (MPC) scheme to deal with two different sources of uncertainties simultaneously, namely, packet losses and external disturbances. In particular, the controller deals with packet losses using a tree-based approach and is robustified against external disturbances using a multiple scenario approach. Finally, the algorithm performance is compared via simulation with other MPC alternatives and a feedback control law.
AB - Systems with elements linked via a communication network are vulnerable to communication problems and attacks of malicious agents, with potentially harmful consequences for performance and stability. This paper proposes a stochastic Model Predictive Control (MPC) scheme to deal with two different sources of uncertainties simultaneously, namely, packet losses and external disturbances. In particular, the controller deals with packet losses using a tree-based approach and is robustified against external disturbances using a multiple scenario approach. Finally, the algorithm performance is compared via simulation with other MPC alternatives and a feedback control law.
KW - Cyber-Physical Systems
KW - Linear Control Systems
KW - Model-based Predictive Control
KW - Robust Control and Stabilization
KW - Stochastic Optimization
UR - http://www.scopus.com/inward/record.url?scp=85142285169&partnerID=8YFLogxK
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U2 - 10.1016/j.ifacol.2022.09.040
DO - 10.1016/j.ifacol.2022.09.040
M3 - Conference article
AN - SCOPUS:85142285169
SN - 2405-8963
VL - 55
SP - 296
EP - 301
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 16
T2 - 18th IFAC Workshop on Control Applications of Optimization, CAO 2022
Y2 - 18 July 2022 through 22 July 2022
ER -