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Numerous engineering complexities are simplified using optimization algorithms. In a solar power system, the necessity of the voltage regulator is obvious. To control the regulator existent research works used PI, PID controllers that might have an unwanted transient response. To overcome such drawbacks here, a fresh scheme is proposed for the designing of the adaptive sliding mode (SM) controller of a solar powered LUO converter using optimization algorithms. The PSO ('Particle Swarm Optimization') is proved to expedite the convergence characteristic for many applications. Here, an ameliorated PSO version is developed. This algorithm is termed the Parameter Improved-PSO (PIPSO) algorithm. In this algorithm, the parameters, say, inertia weight, social along with cognitive agents is updated in every generation. The Proportional Integrator (PI) controller is used. The gain of this controller is tuned using the PIPSO. This algorithm's objective function is to lessen ISE ('Integral Squared Error') of the converter's output voltage. This parameter is picked as the objective function of the optimization algorithm. The proposed PIPSO is established to show better outcomes when contrasted to the traditional PSO concerning tuning a collection of parameters. An analysis is also made to evaluate the effect of usage of the solar panel () in the proposed work. K E Y W O R D S integral squared error, parameter improved particle swarm optimization, proportional integral controller, proportional integral controller, solar panel 1 | INTRODUCTION The continuous growth for global demand and environmental concern has lead to the exploration of Renewable Energy (RE) sources. As contrasted to all other 'RE' sources (Mekhilef, et.al, 2011) the photo-voltaic (PV) energy has advantages such as no noise, cleanliness, along with very less maintenance. The PV (Sangwongwanich, 2018) systems are broadly used for low power electrical generation. This paper attempts to use non-RE as the voltage source input. The DC − DC conversion technology has evolved as a major area of research in the power electronics and drives field. DC − DC converters (Chen et.al, 2017), (Forouzesh et.al, 2017), (Riedel et.al, 2017) are nothing but electrical circuits which can transport the energy to a load. In such sorts of converters, the switches can either be diodes or transistors. The voltage value (transferred) relies upon the switches' duty ratios. The DC − DC converters are extensively utilized on industrial applications along with computers hardware circuits. A disparity of the PI-Derivative (PID) control that utilizes just the proportional and integral terms is called PI control. The PI is further widely utilized than the PID.

Induction motor (IM) drives with direct torque control (DTC) enable fast torque response without the need for complex orientation conversions or inner loop current loop. In the speed estimation responses, however, there is a significant level of torque ripple. The voltage source inverter adds acoustic noise and needs a high sampling frequency since it operates at a high and variable switching frequency. This work describes an ANN-based DTC technique for controlling the speed of an IM drive over a large speed range. To achieve good dynamic performance of induction motor drive, the ANN-based speed controller will replace the speed controller, switching tables, and hysteresis comparators. The neural network was trained using the back-propagation algorithm. The goal of a neural speed controller is to improve the system ability to respond quickly to changes in process variables while also mitigating the impacts of external perturbations. The projected ANN based DTC considerably and simply tracks the reference speed thus improves the efficiency of speed-torque of induction motors with quicker responses for rapid varying of speed reference and torque as that of Electric Vehicles in any uneven roads circumstances. MATLAB/Simulink software is used to evaluate the drive performance for both transient and dynamic operations. The proposed control performance is simulated and compared to a DTC-based traditional PI speed controller. In comparison to PI, the results show that ANN has better and faster effects. The torque ripple gets reduced by 1.5 % in ANN (artificial neural network) controller compared to PI controller. The THD (total harmonic distortion) is reduced by 6.38 % from PI controller to ANN controller.

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