Conventional heating, ventilating, and air conditioning (HVAC) systems have traditionally used the temperature and the humidity ratio as the quantitative indices of comfort in a room. Recently, the carbon dioxide (CO 2 ) concentration has also been recognized as having an important contribution to room comfort. This paper presents the modeling of an augmented HVAC system including CO 2 concentration, and its control strategies. Because the proposed augmented HVAC system is multi-input multi-output (MIMO) and has no relative degree problem, the dynamic extension algorithm can be employed; then, a feedback linearization technique is applied. A linear-quadratic regulator (LQR) is designed to optimize control performance and to stabilize the proposed HVAC system. Simulation results are provided to validate the proposed system model, as well as its linearized control system.

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This paper proposes an integrated photovoltaic (PV) and proton exchange membrane fuel cell (PEMFC) system for continuous energy harvesting under various operating conditions for use with a brushless DC motor. The proposed scheme is based on the incremental conductance (IncCond) algorithm combined with the sliding mode technique. Under changing atmospheric conditions, the energy conversion efficiency of a PV array is very low, leading to significant power losses. Consequently, increasing efficiency by means of maximum power point tracking (MPPT) is particularly important. To manage such a hybrid system, control strategies need to be established to achieve the aim of the distributed system. Firstly, a Matlab/Simulink based model of the PV and PEMFC is developed and validated, as well as the incremental conductance sliding (ICS) MPPT technique; then, different MPPT algorithms are employed to control the PV array under nonuniform temperature and insolation conditions, to study these algorithms effectiveness under various operating conditions. Conventional techniques are easy to implement but produce oscillations at MPP. Compared to these techniques, the proposed technique is more efficient; it produces less oscillation at MPP in the steady state and provides more precise tracking.

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A pulse-width-modulator-(PWM-) based sliding mode controller is developed to study the effects of partial shade, temperature, and insolation on the performance of maximum power point tracking (MPPT) used in photovoltaic (PV) systems. Under partially shaded conditions and temperature, PV array characteristics become more complex, with multiple power-voltage maxima. MPPT is an automatic control technique to adjust power interfaces and deliver power for a diverse range of insolation values, temperatures, and partially shaded modules. The PV system is tested using two conventional algorithms: the Perturb and Observe (P&O) algorithm and the Incremental Conductance (IncCond) algorithm, which are simple to implement for a PV array. The proposed method applied a model to simulate the performance of the PV system for solar energy usage, which is compared to the conventional methods under nonuniform insolation improving the PV system utilization efficiency and allowing optimization of the system performance. The PWM-based sliding mode controller successfully overcomes the issues presented by nonuniform conditions and tracks the global MPP. In this paper, the PV system consists of a solar module under shade connected to a boost converter that is controlled by three different algorithms and is generated using Matlab/Simulink.