IEEE transactions on automatic control, 2017-01, Vol.62 (1), p.177-189
2017
Details
- Autor(en) / Beteiligte
- Titel
- Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection
- Ist Teil von
- IEEE transactions on automatic control, 2017-01, Vol.62 (1), p.177-189
- Ort / Verlag
- New York: IEEE
- Erscheinungsjahr
- 2017
- Link zum Volltext
- Quelle
- IEEE Xplore
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0018-9286, 1558-2523eISSN: 1558-2523DOI: 10.1109/TAC.2016.2537741Titel-ID: cdi_proquest_journals_1855340819
- Format
- –
- 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