TY - GEN
T1 - Model Predictive Control Allocation of Systems with Different Dynamics
AU - Kissai, Moad
AU - Monsuez, Bruno
AU - Mouton, Xavier
AU - Martinez, Didier
AU - Tapus, Adriana
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Several systems are integrated in passenger cars. Some of them are just redundant systems due to safety requirements. Others, are completely different and can interact with each other as long as they are operating inside the same vehicle. Control allocation methods have been successfully implemented in advanced aircrafts to avoid conflicts, especially in the context of redundant systems. In this paper, we will rather focus on coordinating non-redundant advanced chassis systems with different dynamics. This difference in dynamics can be especially problematic when systems exhibit different communication delays. Model Predictive Control Allocation (MPCA) methods are therefore investigated in order to activate the right system at the right moment. Results show that particularly when the most effective system is saturated, another system with a different time delay can be activated few steps before saturation to instantly take over the maneuver. With good knowledge of actuator dynamics and higher computation power, MPCA methods are able to solve complex problems in severe situations.
AB - Several systems are integrated in passenger cars. Some of them are just redundant systems due to safety requirements. Others, are completely different and can interact with each other as long as they are operating inside the same vehicle. Control allocation methods have been successfully implemented in advanced aircrafts to avoid conflicts, especially in the context of redundant systems. In this paper, we will rather focus on coordinating non-redundant advanced chassis systems with different dynamics. This difference in dynamics can be especially problematic when systems exhibit different communication delays. Model Predictive Control Allocation (MPCA) methods are therefore investigated in order to activate the right system at the right moment. Results show that particularly when the most effective system is saturated, another system with a different time delay can be activated few steps before saturation to instantly take over the maneuver. With good knowledge of actuator dynamics and higher computation power, MPCA methods are able to solve complex problems in severe situations.
KW - Chassis Systems
KW - Identification
KW - Model Predictive Control Allocation
KW - Robust Control
KW - Vehicle Dynamics
UR - https://www.scopus.com/pages/publications/85076814354
U2 - 10.1109/ITSC.2019.8917438
DO - 10.1109/ITSC.2019.8917438
M3 - Conference contribution
AN - SCOPUS:85076814354
T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
SP - 4170
EP - 4177
BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Y2 - 27 October 2019 through 30 October 2019
ER -