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%.

The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and parametric variations. It depicts undesirable phenomenon like chattering, inherent in it causing power and heat losses. In this paper, a supertwisting sliding mode algorithm based nonlinear robust controller has been designed for MPPT of a PV system which not only removes the chattering but also enhances the overall system’s dynamic response. Moreover, supertwisting sliding mode controller is robust against changing environmental conditions like change in temperature and irradiance. Noninverting DC-DC Buck-Boost converter has been used as an interface between source and the load. The efficiency of MPPT of a PV system depends upon the accuracy of reference for peak power voltage, therefore an efficient mechanism for reference generation has also been proposed in this work. The reference for peak power voltage has been generated by using a trained artificial neural network, which is to be tracked by proposed nonlinear controllers. Sliding mode controller (SMC) and synergetic controllers have also been designed for MPPT of a PV system in order to compare them with supertwisting sliding mode controller (ST-SMC). Global asymptotic stability of the system has been ensured by using Lyapunov stability criterion. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink ODE 45 environment. ST-SMC has also been compared with recently proposed integral backstepping controller and other conventional MPPT controllers given in the literature. The simulation results show the better performance of ST-SMC in terms of best dynamic response and robustness.

The public awareness about global warming, emission of green-house gases and depletion of natural resources like oil and natural gas, are the main factors due to which fuel cell hybrid electric vehicles (FHEVs) have attained importance in automotive industry. Hard driving conditions like steep areas, slippery roads and rough terrains boost up the nonlinearities present in vehicle’s model. The considered unified mathematical model of FHEV is based on fuel cell as a primary source, ultracapacitor and battery as storage units as well as the induction motor dynamics. The variations in parameters like resistance, capacitance, inductance and the nonlinearities of the dynamical system have also been considered. Three adaptation based nonlinear controllers namely adaptive terminal sliding mode, adaptive terminal synergetic and adaptive synergetic controllers have been proposed for the regulation of DC bus voltage along with speed tracking when subjected to European extra urban driving cycle. Lyapunov stability theory has been used to ensure global asymptotic stability of the system. Proposed controllers have been simulated on MATLAB/Simulink, where their comparison has been presented with each other and with recently proposed nonlinear controllers in the literature. Furthermore, ATSMC has further been implemented on real-time microcontroller hardware in the loop setup. The experimental results show that it provides better performance.

Brain Tumor is an unwanted mass when cells grow abnormally. It puts pressure on certain areas of the brain which dangerously affects its functioning. For severe tumors like malignant, surgery is not a good option because they do not have definite boundaries and are closely attached with the other healthy tissues of the brain. So chemotherapy is preferable in this case to avoid damaging the healthy cells. The main purpose of this research is to suggest a controller for drug injection so that the tumor cells may reach the desired reference value of zero. In this paper, variable structure based nonlinear control algorithms: sliding mode control, integral sliding mode control, double integral sliding mode control and super-twisting sliding mode controllers have been proposed to reduce the tumor cells, maintain a safe number of healthy cells, keep the immune cells above a certain value and ensure suitable amount of chemotherapy drug. Lyapunov based stability theory has been used to prove stability of SMC, ISMC, DISMC and ST-SMC. MATLAB/Simulink environment has been used to analyze the performance of the proposed controllers on the basis of chattering, rate of convergence, undershoot/overshoot etc. and on the basis of these results drug through most suitable controller is advised. Among the proposed controllers, due to its better convergence, reduced chattering and less amount of drug used, ST-SMC is suggested for the chemotherapy of brain tumor.

Lack of insulin production by pancreas causes high blood glucose level (BGL) in the diabetic patients. For their treatment, manual insulin intake is possible only during the day timings but not feasible during the night when the patient is sleeping. Artificial pancreas (AP) is used for the automatic regulation of BGL by continuous injection of insulin. The nonlinear Bergman’s Minimal Model (BMM) considers fixed meal disturbance which may actually vary continuously during medication due to meal intake or by doing exercise. This variation has been taken into account by the Extended Bergman’s Minimal Model (EBMM). In this paper, two nonlinear: Terminal Synergetic and State Feedback Linearization based controllers have been proposed for AP to regulate BGL using EBMM. Asymptotic stability of the proposed controllers has been proved using Lyapunov theory. Comparison of the proposed controllers with each other and that with PID controller has been done using MATLAB/Simulink. White noise has been added as the disturbance to further analyze the output performance of the proposed controllers. The Terminal Synergetic controller which performs better than others, has also been implemented on the data of six Type 1 diabetic patients available in the literature.