TY - GEN
T1 - A Machine Learning Approach for Tuning Model Predictive Controllers
AU - Ira, Alex S.
AU - Shames, Iman
AU - Manzie, Chris
AU - Chin, Robert
AU - Nesic, Dragan
AU - Nakada, Hayato
AU - Sano, Takeshi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/18
Y1 - 2018/12/18
N2 - Many industrial domains are characterized by Multiple-Input-Multiple-Output (MIMO) systems for which an explicit relationship capturing the nontrivial trade-off between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closed-loop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the human-learned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.
AB - Many industrial domains are characterized by Multiple-Input-Multiple-Output (MIMO) systems for which an explicit relationship capturing the nontrivial trade-off between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closed-loop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the human-learned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.
UR - http://www.scopus.com/inward/record.url?scp=85060831080&partnerID=8YFLogxK
U2 - 10.1109/ICARCV.2018.8581227
DO - 10.1109/ICARCV.2018.8581227
M3 - Conference contribution
T3 - 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
SP - 2003
EP - 2008
BT - 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
Y2 - 18 November 2018 through 21 November 2018
ER -