The operating principle of condensing boilers is based on exploiting heat from flue gases to pre-heat cold water at the inlet of the boiler: by condensing into liquid form, flue gases recover their latent heat of vaporization, leading to 10–12% increased efficiency with respect to traditional boilers. However, monitoring the energy efficiency of condensing boilers is complex due to their nonlinear dynamics: currently, (static) nonlinear efficiency curves of condensing boilers are calculated at quasi-stationary regime and ‘a posteriori’, i.e. from data collected during chamber tests: therefore, with this static approach, it is possible to monitor the energy efficiency only at steady-state regime. In this work we propose a novel model-based monitoring approach for condensing boilers that extends the operating regime for which monitoring is possible: the approach is based on a hybrid dynamic model of the condensing boiler, where state-dependent switching accounts for dynamically changing condensing/non condensing proportions. Monitoring the energy efficiency over the boiler's complete dynamic regime is possible via switching estimators designed for the different condensing/non condensing modes. By using real-world boiler efficiency data we show that the proposed approach results in a (dynamic) nonlinear efficiency curve which gives a more complete description of the condensing boilers operation than static nonlinear efficiency curves: in addition, the dynamic curve can be derived ‘a priori’, i.e. from first principles, or from data collected during normal boiler operation (without requiring special chamber tests).

In this paper, an adaptive control scheme for Unmanned Aerial Vehicles (UAVs) path following under slowly time-varying wind is developed. The proposed control strategy integrates the path following law based on the vector field method with an adaptive term counteracting the effect of wind's unknown component. In particular, it is shown that the path following error is bounded under slowly time-varying unknown wind and converges to zero for unknown constant wind. Numerical simulations illustrate that, in environments with unknown and slowly time-varying wind conditions, the proposed method compensates for the lack of knowledge of the wind vector, and attains a smaller path following error than state-of-the-art vector field method.

Energy efficiency in building heating, ventilating and air conditioning (HVAC) equipment requires the development of accurate models for testing HVAC control strategies and corresponding energy consumption. In order to make the HVAC control synthesis computationally affordable, such control-oriented models are typically a simplified version of more elaborate building simulation environments (e.g. EnergyPlus, Modelica, TRNSYS). Despite their simplicity, control-oriented models must effectively catch all the interactions between HVAC equipment, in order achieve system integration and avoid fragmentation in HVAC modelling and control synthesis, which is one of the main causes of suboptimal performance in the building sector. In this work we propose an integrated control-oriented modelling for HVAC performance benchmarking. The following HVAC equipment and their interaction are modelled: condensing boiler, radiator, air handling unit, heat pump, chiller, fan, pump, pipe, duct, and multiple thermal zones. The proposed modelling approach is modular so that the user can add and remove as many components as desired, depending on the building to be modelled. Appropriate procedures for synthesis of control strategies and benchmarking are proposed. Extensive simulations demonstrate the effectiveness of the proposed methodology, which can lead to improvements in occupants' thermal comfort while at the same time consistently attaining energy savings.