Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

Conventional Energy Resources (CER) are being rapidly replaced by Renewable Energy Resources (RER) due to their abundant, environmentally friendly, clean, and inexhaustible nature. In recent years, Solar Photovoltaic (SPV) energy installation is booming at a rapid rate among various RER. Grid-Connected PVS required advance DC-link controllers to overcome second harmonic ripple and current controllers to feed-in high-quality current to the grid. This paper successfully presents the design of a Fuzzy-Logic Based PI (F-PI) and Fuzzy-Logic based Sliding Mode Controller (F-SMC) for the DC-link voltage controller and Proportional Resonant (PR) with Resonant Harmonic Compensator (RHC) as a current controller for a Single-Phase Two-Stages Grid-connected Transformerless (STGT) Photovoltaic (PV) Inverter. The current controller is designed with and without a feedforward PV power loop to improve dynamics and control. A Second Order General Integral (SOGI)-based Phase Lock Loop (PLL) is also designed that has a fast-dynamic response, fast-tracking accuracy, and harmonic immunity. A 3 kW STGT-PV system is used for simulation in Matlab/Simulink. A comparative assessment of designed controllers is carried out with a conventionally well-tuned PI controller. The designed controllers improve the steady-state and dynamic performance of the grid-connected PV system. In addition, the results, performance measure analysis, and harmonics contents authenticate the robustness, fastness, and effectiveness of the designed controllers, related to former works.

No abstract

Diabetes Mellitus Type 1 happens when our immune system destroys beta cells in our pancreas due to which it fails to produce enough insulin; a hormone which allows sugar/glucose to enter in its cells in order to produce energy. To cope with failure of pancreas, artificial ones are used to inject the required amount of insulin in the body. Controllers are used for automatic balancing of blood glucose-insulin level. Bergman's minimal model (BMM) is a physiologically verified model representing this phenomenon. In a recent research BMM is extended to more generic form with an extended state of the system, dealing with the disturbance to the blood glucose level caused by meal intake during medication. In this research paper, we have used BMM along with its extended model and proposed three nonlinear controllers: Integral Backstepping (IBS) Controller, Backstepping (BS) Controller and Fuzzy Logic Controller (FLC), for the automatic stabilization of the blood glucose level in Diabetes Mellitus Type 1 patients. The integral action is integrated with Backstepping technique; resulting in reducing steady-state error by significant amount. A mathematical analysis has been done to prove the asymptotic stability of the proposed controllers for the both models using Lyapunov theory. For showing the tracking behavior of the proposed controllers with their respective models to the desired blood-glucose output, simulation results have been performed and discussed using MATLAB/Simulink. Comparison results of the both systems show that the proposed controllers performs far better than the ones given in the literature. INDEX TERMS Bergman's minimal model (BMM), integral backstepping (IBS), backstepping (BS), fuzzy logic controller (FLC).

No abstract