Grey Wolf Optimizer Based Super Twisted Sliding Mode Controller (STSMC) for Self-balancing Control of a Two-wheel Electric Scooter

Abstract

Transportation is a rapidly expanding industry nowadays. Self-balancing personal transporter scooters were launched in response to the fast growth in demand for personal transporter vehicles. The two-wheel self-balancing scooter is based on an inverted pendulum system and this system is an unstable and nonlinear system. An inertial measurement unit (IMU) is a combination of an accelerometer and a gyroscope measurement used to estimate and obtain the tilt angle of the scooter. The super twisted sliding mode controller (STSMC) is used to balance the scooter by correcting the difference between the desired set point and the actual tilt angle and adjusting the direct current (DC) motor speed correspondingly. When the scooter is tilted forward, the motor moves forward to catch up, to balance the scooter. A proportional integral derivative (PID) controller is used to control the direction of the scooter. That means turning left or right. The STSMC parameters and PID parameters are tuned using Grey Wolf Optimization (GWO), Particle swarm optimization (PSO) and genetic algorithm (GA) for compression, and the controller?��?s dynamic performance evaluation is done using MATLAB/Simulink. Accordingly, the GWO based STSMC for balancing control of scooters has a settling time of 0.2438sec, a rise time of