This report proposes a new two-stage optimization method for robust Model Predictive Control (RMPC) with Gaussian disturbance and state estimation error. Since the disturbance is unbounded, it is impossible to achieve zero probability of violating constraints. Our goal is to optimize the expected value of a objective function while limiting the probability of violating any constraints over the ...

[Purpose] The purpose of this study was to investigate the effects of virtual reality training combined with modified constraint-induced movement therapy on upper extremity motor function recovery in acute stage stroke patients. [Subjects and Methods] Four acute stage stroke patients participated in the study. A multiple baseline single subject experimental design was utilized. Modified constra...

In this work we present explicit definitions for the sample complexity associated with the Sample Average Approximation (SAA) Method for instances and classes of multistage stochastic optimization problems. For such, we follow the same notion firstly considered in Kleywegt et al. (2001). We define the complexity for an arbitrary class of problems by considering its worst case behavior, as it is...

In this work we present explicit definitions for the sample complexity associated with the Sample Average Approximation (SAA) Method for instances and classes of multistage stochastic optimization problems. For such, we follow the same notion firstly considered in Kleywegt et al. (2001). We define the sample complexity for an arbitrary class of problems by considering its worst case behavior, a...