TY - JOUR
T1 - Resource-aware MPC for constrained linear systems: two rollout approaches
AU - Gommans, T.M.P.
AU - Theunisse, T.A.F.
AU - Antunes, D.J.
AU - Heemels, W.P.M.H.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - In systems with resource constraints, such as actuation limitations in sparse control applications or limited bandwidth in networked control systems, it is desirable to use control signals that are either sparse or sporadically changing in time. Motivated by these applications, in this paper we propose two resource-aware MPC schemes for discrete-time linear systems subject to state and input constraints. The two MPC schemes exploit ideas from rollout strategies to determine simultaneously the new (continuous) control inputs and the (discrete) time instants at which the control actions are updated. The first scheme provides performance guarantees by design, in the sense that it allows the user to select a desired suboptimal level of performance, where the degree of suboptimality provides a trade-off between the guaranteed closed-loop control performance on the one hand and the utilization of (communication/actuation) resources on the other hand. The second scheme provides a guaranteed (average) resource utilization, while cleverly allocating these resources in order to maximize the control performance. By means of numerical examples, we demonstrate the effectiveness of the proposed strategies.
AB - In systems with resource constraints, such as actuation limitations in sparse control applications or limited bandwidth in networked control systems, it is desirable to use control signals that are either sparse or sporadically changing in time. Motivated by these applications, in this paper we propose two resource-aware MPC schemes for discrete-time linear systems subject to state and input constraints. The two MPC schemes exploit ideas from rollout strategies to determine simultaneously the new (continuous) control inputs and the (discrete) time instants at which the control actions are updated. The first scheme provides performance guarantees by design, in the sense that it allows the user to select a desired suboptimal level of performance, where the degree of suboptimality provides a trade-off between the guaranteed closed-loop control performance on the one hand and the utilization of (communication/actuation) resources on the other hand. The second scheme provides a guaranteed (average) resource utilization, while cleverly allocating these resources in order to maximize the control performance. By means of numerical examples, we demonstrate the effectiveness of the proposed strategies.
KW - Model predictive control
KW - Networked control systems
KW - Sparse control
UR - http://www.scopus.com/inward/record.url?scp=85009895034&partnerID=8YFLogxK
U2 - 10.1016/j.jprocont.2016.12.004
DO - 10.1016/j.jprocont.2016.12.004
M3 - Article
AN - SCOPUS:85009895034
SN - 0959-1524
VL - 51
SP - 68
EP - 83
JO - Journal of Process Control
JF - Journal of Process Control
ER -