Economic model predictive control with triggered evaluations: State and output feedback

In this work, we focus on the computation load reduction in the optimization of economic model predictive control (EMPC) for nonlinear systems. Specifically, event-based triggering approach is adopted to significantly reduce the number of evaluations of the EMPC. First, we consider the case that state feedback is available and design a triggering condition based on the difference between the actual system state and its predicted value. At a sampling time, if the triggering condition is satisfied, the EMPC is re-evaluated. Subsequently, we consider the case that only output feedback is available. In this case, a robust moving horizon estimator is used to reconstruct the state information from output measurements and the corresponding triggering condition is based on the difference between the measured and predicted output as well as its time derivatives. For both cases, the EMPC is redesigned to account for potential open-loop operations. Sufficient conditions that ensure the closed-loop stability are provided for both cases. A chemical process is used to illustrate the effectiveness of the proposed designs.