TY - GEN
T1 - Probabilistic Discrete Time Robust H2 Controller Design
AU - Chamanbaz, Mohammadreza
AU - Sznaier, Mario
AU - Lagoa, Constantino
AU - Dabbene, Fabrizio
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - Optimal {{\mathcal{H}}_2} control theory is appealing, since it allows for optimizing a performance index frequently arising in practical situations. Moreover, in the state feedback case, the resulting closed loop system has an infinite gain margin and a phase margin of at least 60o. However, these properties no longer hold in the output feedback case, where it is well known that there exist cases where the system is arbitrarily fragile. Motivated by this observation, since the early 1980's a large research effort has been devoted to the problem of designing robust {{\mathcal{H}}_2} controllers. To this effect several relaxations of the original problem have been introduced, but all of these lead to conservative solutions. Surprisingly, the original problem remains, to date, still open. To address this issue, in this paper we present a randomization based algorithm that seeks to solve a relaxation of the original problem. Contrary to existing approaches, the performance of the resulting controller can be made - in a sense precisely defined in the paper - arbitrarily close to the optimal one. These results are illustrated with an academic example.
AB - Optimal {{\mathcal{H}}_2} control theory is appealing, since it allows for optimizing a performance index frequently arising in practical situations. Moreover, in the state feedback case, the resulting closed loop system has an infinite gain margin and a phase margin of at least 60o. However, these properties no longer hold in the output feedback case, where it is well known that there exist cases where the system is arbitrarily fragile. Motivated by this observation, since the early 1980's a large research effort has been devoted to the problem of designing robust {{\mathcal{H}}_2} controllers. To this effect several relaxations of the original problem have been introduced, but all of these lead to conservative solutions. Surprisingly, the original problem remains, to date, still open. To address this issue, in this paper we present a randomization based algorithm that seeks to solve a relaxation of the original problem. Contrary to existing approaches, the performance of the resulting controller can be made - in a sense precisely defined in the paper - arbitrarily close to the optimal one. These results are illustrated with an academic example.
UR - http://www.scopus.com/inward/record.url?scp=85099878922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099878922&partnerID=8YFLogxK
U2 - 10.1109/CDC42340.2020.9304278
DO - 10.1109/CDC42340.2020.9304278
M3 - Conference contribution
AN - SCOPUS:85099878922
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2240
EP - 2245
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -