Sukkur IBA Journal of Emerging Technologies

Modeling and Control of a Snake Robot using SimMechanics

Beenish Ansari — 2024-01-29

Snake robots are biological inspired robots designed to locomote in an unwanted environment. The mechanism comprises of serially connected links through rotational joints capable of rotating its body in one or more plans. In this paper, snake robot model is proposed. The proposed model consists of a 10-links physically connected through joints. The aim of this work is to propel a snake robot consisting of 10-link through the n-1 rotary joint on the serpentine aisle. Structure of the entire mechanical model of a snake is created in Simmechanics, MATLAB library tool within Simulink environment. The mechanics for the motion of snake robot including dynamics, kinematics and friction force models are developed in order to find the nth link location in a 3D environment. In addition, we have also established the joint angle to activate the robot’s link on a serpenoid terrain. To control the entire movement comprising position, speed and torque at each joint of the snake robot, control system is altered. This system is further composed of 3 proportional controllers having a sensor to take measurement results from an actuator. The simulation studies showing performance of the system are conducted to determine the control parameters of the system measured through joint sensor via scope, graph and display units. From simulation results it is shown that the snake robot followed the serpentine wave motion.

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

Umair Rashid — 2024-04-15

A next-generation electrical power system known as the "Smart Grid" (SG) uses two-way communication to generate, use, and transport electrical energy. Demand Response (DR) is one of the SG's primary features (DR). Smart meters in DR transmit a pricing signal to the consumer, allowing them to adjust their demand in reaction to the corresponding price signals. Day-Ahead and Time-of-Use are the most widely used load scheduling schemes, however, they deviate from the Real time pricing scheme (RTP). Due to its erratic nature, integrating renewable energy sources like solar and wind is a difficult process in SG. Because of the fluctuations in both energy consumption patterns and power rates, the majority of current methods for managing demand are predicated either on day-ahead or time-of-use pricing rather than real-time pricing. This study describes a load scheduling system that uses a Genetic Algorithm (GA) to classify various users according to their energy use in a real-time pricing environment. Our load scheduling problem is formulated utilizing the knapsack mathematical formulation technique to reduce the electricity expenditure. In order to keep the grid stable and reduce costs, renewable energy is integrated with the grid's energy to lower the Peak-to-Average Ratio (PAR). The efficiency of the suggested algorithm in terms of electricity cost and PAR reduction is supported by simulation results.