Efficient Output Feedback MPC for NCS With Data Dropout and Bounded Disturbance via Adaptive Event-Triggered Control

This paper studies efficient output feedback model predictive control (OFMPC) via adaptive event-triggered control (AETC) for the networked control system (NCS) with data dropout and bounded disturbance. First, we adopt AETC in NCS to save limited network resources and introduce a Bernoulli random variable to represent the occurrence of data dropout events. Subsequently, two sufficient conditions are presented to handle the gain matrix of the state observer and the estimation error bound. Then, by offline solving an elliptic invariant set satisfying the input constraint and online optimizing for additional perturbations, the initial feasible set is enlarged and the online computational burden is greatly reduced. Finally, the effectiveness of the proposed algorithm is verified by two simulation examples.