In this paper, we explain the design and implementation of an intelligent irrigation control system based on fuzzy logic for the automatic control of water pumps used in farms and greenhouses. This system enables its user to save water and electricity and prevent over-watering and under-watering of the crop by taking into account the climatic parameters and soil moisture. The irrigation system works without human intervention. The climate sensors are packaged using electronic circuits, and the whole is interfaced with an Arduino and a Simulink model. These sensors provide information that is used by the Simulink model to control the water pump speed; the speed of the water pump is controlled to increase or decrease the amount of water that needs to be pushed by the pump. The Simulink model contains the fuzzy control logic that manages the data read by the Arduino through sensors and sends the command to change the pump speed to the Arduino by considering all the sensor data. The need for human intervention is eliminated by using this system and a more successful crop is produced by supplying the right amount of water to the crop when it is needed. The water supply is stopped when a sufficient amount of moisture is present in the soil and it is started as soon as the soil moisture levels drops below certain levels, depending upon the environmental factors.

This paper shows the novel design of Eddy Current Braking System (ECBS) using soft computing techniques mainly FLC and PSO for the validation of experimental results of ECBS. Eddy Current Brakes are used in many applications where superior braking performance at high speed is desired. Eddy current brakes being frictionless and contactless offer numerous advantages over conventional mechanical brakes. The ECBS can be implemented in aircraft arreter barrier system as energy absorber for optimum braking. The hardware model of ECBS has been developed, simulated, and analysed. Performance of ECBS was optimized using soft computing techniques such as FLC and PSO on the MATLAB platform and the control dynamics of the system was analysed. It was observed that PSO based ECBS gives better result in comparison to FLC based ECBS in terms of settling time and peak overshoot.

Aircraft Arrester Barrier Systems (AABS) are very important for the purpose of arresting combat aircrafts due to aborted take-off and emergency landings. This system is installed near the end of runways. Energy Absorbing System (EAS) is one of the important subsystems of AABS. There is no control on energy absorption during operation of an aircraft emergency arrestment in conventional EAS. It is highly nonlinear in nature and these are installed as per the capacity of the aircraft. To overcome this problem, we may use the eddy current braking system (ECBS) for the energy absorber. In proposed ECBS, braking torque is high and controllable so that any weight capacity of aircraft can stop. In this paper, the prototype of eddy current brakes has been designed and developed for energy absorbing system of AABS.

To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work.