Model-Free Adaptive Nonlinear Control of the Absorption Refrigeration System

Based on the model-free adaptive control (MFAC) theory, the temperature tracking control problem of single-effect LiBr/H 2 O absorption chiller is explored. Due to the complex nonlinearity and strong coupling characteristics of the absorption refrigeration system, model-free adaptive control strategy is designed for its temperature tracking control. Nevertheless, the traditional model-free adaptive control has a slow tracking speed and poor denoising ability. In order to improve its control effect, output error rate is added to the objective function and new control laws of model-free adaptive control with output error rate (MFAC-OER) have been derived through an exhaustive convergence and stability analysis. The input and output information of the absorption refrigeration system, namely the hot water pump frequency and frozen water outlet water temperature, are combined. The data model of the absorption refrigeration system is subsequently deduced using a compact format dynamic linearization method. Next, based on the single effect absorption chiller experimental platform in our laboratory, its sixth-order dynamic model is built. Finally, the effectiveness and practicability of the improved control strategy are validated by numerical simulations and experimental operating data from our laboratory as well as by the dynamical model of the absorption chiller.

Combined Energy and Comfort Optimization of Air Conditioning System in Connected and Automated Vehicles

In this paper, we propose a combined energy and comfort optimization (CECO) strategy for the air conditioning (A/C) system of the connected and automated vehicles (CAVs). By leveraging the weather and traffic predictions enabled by the emerging CAV technologies, the proposed strategy is able to minimize the A/C system energy consumption while maintaining the occupant thermal comfort (OTC) within the comfort constraints, where the comfort is quantified by a modified predictive mean vote (PMV) model adapted for an automotive application. A general CECO problem is formulated and addressed using model predictive control (MPC) and weather/traffic previews. Depending on the ways of exploiting the preview information and enforcing the OTC constraint, different MPCs are developed based on solving different variations of the general CECO problem. The CECO-based MPCs are then tested in simulation using an automotive A/C system simulation model (CoolSim) as the virtual testbed. The simulation results show that, over SC03 driving cycle, the proposed CECO-based MPCs outperform the baseline cabin temperature tracking controller, reducing the A/C system energy consumption by up to 7.6%, while achieving better OTC according to the PMV-based metrics. This energy saving in A/C system translates to 3.1% vehicle fuel economy improvement. The trade-off between energy efficiency and OTC for different control scenarios is also highlighted.

Instability Evaluation of the Temperature Tracking and Transmission Corridors

The developing improvements in electrical power physical body functional demands ask for a spread control center that is actually decentralized, incorporated, versatile, and additionally readily available. Modern nerve center is in fact transferring that direction along with differing degrees of results. Device broad installed phasor dimension devices deliver their determined information to a main personal computer, where snapshots of the compelling device actions are offered online. This brand-new high quality of body details opens up a variety of new apps to assess and also actively preserve unit's security just in case of voltage, slant or even regularity weakness, thermic overload, and oscillations. Recent established protocols as well as their layout for these application regions are offered