Making a prediction typically involves dealing with uncertainties. The application of uncertainty analysis to buildings and HVAC (heating, ventilation and air conditioning) systems, however, remains limited. Most existing studies concentrate on the parameter uncertainty and parametric variability in building simulations for the design stage, and rely on Monte Carlo experiments to quantify this ...

A methodology for combining multi-parametric programming and NCO tracking is presented in the case of linear dynamic systems. The resulting parametric controllers consist of (potentially nonlinear) feedback laws for tracking optimality conditions by exploiting the underlying optimal control switching structure. Compared to the classical multi-parametric MPC controller, this approach leads to a ...

The effects of parametric uncertainty in stable state space systems are analyzed by averaging the state covariance over the statistics of the uncertain parameters. For natural frequency uncertainty, this computation is related to the Fourier transform of the probability density function of the uncertain parameter. Equipartition and incoherence are illustrated for a single mode oscillator. Avera...

Reliability analysis is one of the significant applications of statistics. Many parametric and non-parametric techniques are available for reliability estimation based on precise life time observations, without considering the uncertainty of individual observations. But life time observations are not precise measurements but more or less fuzzy. Therefore by considering life time measurements as...

Robust explicit/multi-parametric controllers are designed for constrained, linear discrete–time systems with box-constrained states and inputs, involving uncertainty in the left–hand side (LHS) of the Model Predictive Control (MPC) optimization model. Based on previous results, this work presents a new algorithm that features: (i) a dynamic programming reformulation of the MPC optimization, (ii...

This note proposes a robust control law for d i n k robotmanipulators with parametric uncertainty whose upper bound is notassumed to be known. The proposed robust control based on the Cor-less-Leitmann approach includes a simple estimation law for the upperbound on the parametric uncertainty and an additional control inputtobe updated as a function of the estimated value...