Abstract— This paper proposes a novel two-stage optimization method for robust Model Predictive Control (RMPC) with Gaussian disturbance and state estimation error. Since the dis...
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem b...
Bridging the gap between model-based design and platformbased implementation is one of the critical challenges for embedded software systems. In the context of embedded control sy...
Truong Nghiem, George J. Pappas, Rajeev Alur, Anto...
We review state-space control models in order to identify timing properties that can favour flexible scheduling of real-time control tasks. First, from the state-space model of a ...
Predictive state representations (PSRs) are a recently proposed way of modeling controlled dynamical systems. PSR-based models use predictions of observable outcomes of tests that...