Event-based reduced-attention predictive control for nonlinear uncertain systems

Event-based control is an alternative to traditional control where new measurements are sampled only if critical events occur. This not only allows to reduce the control effort but it satisfies nowadays application requirements, such for example reduction of information exchange, computational power, or energy consumption. The work in this field is, however, still sparse and only a few results are available. Properly choosing an event-detection logic can considerably improve the overall system´s performance. We propose a control algorithm which makes use of a model-based triggering strategy to reduce the control effort (reduced-attention control), while guaranteeing robustness against bounded additive perturbations for nonlinear continuous time systems. In particular, we derive conditions which guarantee that asymptotic stability of the nominal system implies practical stability of the real one in a neighborhood of the origin. A continuous stirred tank reactor is used as a benchmark problem to show the effectiveness of the presented algorithm.