TY - JOUR
T1 - Aperiodic event-triggered model predictive control for perturbed LTI systems
T2 - A PID based approach
AU - He, Ning
AU - Li, Yuxiang
AU - Li, Huiping
AU - Xu, Zhongxian
N1 - Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/11
Y1 - 2022/11
N2 - A new event-triggered model predictive control (ET-MPC) framework is proposed for perturbed linear time-invariant (LTI) systems with input and state constraints. Firstly, we propose a novel aperiodic sampling strategy, which determines the upcoming update instants based on the proportion, integral and differential (PID) of the error between the actual state and its optimal prediction to effectively reduce the computational and communication burden of the system. Then, a PID based ET-MPC algorithm is developed and its feasibility as well as the closed-loop stability are strictly proved. Finally, simulation and comparison studies are conducted based on the cart-damper-spring system to illustrate the effectiveness of the proposed method.
AB - A new event-triggered model predictive control (ET-MPC) framework is proposed for perturbed linear time-invariant (LTI) systems with input and state constraints. Firstly, we propose a novel aperiodic sampling strategy, which determines the upcoming update instants based on the proportion, integral and differential (PID) of the error between the actual state and its optimal prediction to effectively reduce the computational and communication burden of the system. Then, a PID based ET-MPC algorithm is developed and its feasibility as well as the closed-loop stability are strictly proved. Finally, simulation and comparison studies are conducted based on the cart-damper-spring system to illustrate the effectiveness of the proposed method.
KW - Closed-loop stability
KW - Event-triggered control
KW - Model predictive control
KW - PID strategy
UR - http://www.scopus.com/inward/record.url?scp=85140327245&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2022.10.055
DO - 10.1016/j.ins.2022.10.055
M3 - 文章
AN - SCOPUS:85140327245
SN - 0020-0255
VL - 616
SP - 141
EP - 156
JO - Information Sciences
JF - Information Sciences
ER -