Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection

In this paper, we propose a self-triggered formulation of model predictive control for continuous-time nonlinear input-affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into sever...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 62; H. 1; S. 177 - 189
Hauptverfasser: Hashimoto, Kazumune, Adachi, Shuichi, Dimarogonas, Dimos V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Actuators
Adaptive Control
Adaptive control systems
Adaptive sampling
Adaptive systems
Communication load
Continuous time systems
Control methods
Control tasks
Dynamical systems
Event-Triggered control
Event-triggered controls
Feedback control
Linear systems
Local state feedback
Model predictive control
model predictive control (MPC)
Network control
Networked control systems
Nonlinear control
Nonlinear inputs
nonlinear systems
Optimal control
Optimal controls
Predictive control
Predictive control systems
Simulation example
Stability analysis
State feedback
Trajectory
Trajectory control
Trajectory optimization
ISSN:0018-9286, 1558-2523, 1558-2523
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Beschreibung
Zusammenfassung:In this paper, we propose a self-triggered formulation of model predictive control for continuous-time nonlinear input-affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-and-hold implementation is also given, which guarantees that the state converges to a terminal region where the system can be stabilized by a local state feedback controller. Some simulation examples validate our proposed framework.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9286
1558-2523
1558-2523
DOI:10.1109/TAC.2016.2537741