Robust model predictive control with aperiodic actuation

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We consider a control design problem using wireless sensor/actuator networks. Such systems need to operate within the limited resources of available battery life and bandwidth. To address these concerns, we take a model predictive control (MPC) approach for perturbed LTI systems with constraints on the admissible input and state sets. We propose a triggering mechanism (TM) that aims to reduce the number of MPC updates, with the goal to reduce the communication and computation loads. The TM uses trajectories that have been computed at the last update instant and a current measurement to determine whether or not to trigger an update. The TM consists of two parts: 1) inequalities that are functions of the error signal between the observed states and the predicted trajectories, guaranteeing recursive feasibility, and 2) a scalar inequality, that is a function of a weighted version of the value function at the last triggering instant, guaranteeing closed-loop convergence. Numerical simulations demonstrate the effectiveness of our TM in reducing the number of MPC updates, thereby possibly reducing the communication load as well.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE 56th Annual Conference on Decision and Control (CDC)
EditorsA Astolfi et al
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-150902873-3
Publication statusPublished - 2017
EventCDC 2017: 56th IEEE Annual Conference on Decision and Control - Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017


ConferenceCDC 2017: 56th IEEE Annual Conference on Decision and Control
OtherThe CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.
Internet address


  • Event-triggered control
  • Robust model predictive control
  • Wireless sensor/actuator networks


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