Super Twisted Sliding Mode Controller for Self-balancing Control of a Two-Wheeled Electric Scooter Based on Grey Wolf Optimizer
| dc.contributor.advisor | Lebsework Negash | |
| dc.contributor.author | Yalew Balkew Kebede | |
| dc.date.accessioned | 2026-06-20T15:27:40Z | |
| dc.date.available | 2026-06-20T15:27:40Z | |
| dc.date.issued | 2026-02-01 | |
| dc.description.abstract | The rapid expansion of the transportation industry has intensified the demand for ef-ficient, reliable, and intelligent personal mobility solutions. Self-balancing two-wheeled scooters have emerged as an innovative response to this need, offering compact, energy efficient, and environmentally friendly transportation. However, ensuring stability and robustness in such systems remains a major challenge due to their nonlinear, time-varying, and inherently unstable dynamics, especially under external disturbances and load varia tions.To address this gap, this study proposes a robust control strategy for a self-balancing two-wheeled personal transporter modeled as an inverted pendulum system. The main objective is to design and evaluate a Super Twisting Sliding Mode Controller (STSMC) capable of maintaining balance and directional stability under varying load and distur bance conditions. The STSMC was chosen because it effectively eliminates the chattering phenomenon associated with conventional Sliding Mode Control (SMC) while retaining its strong robustness and finite-time convergence properties—making it particularly suitable for highly nonlinear and uncertain systems such as self-balancing scooters.To enhance per formance, the STSMC parameters are optimized using two metaheuristic algorithms Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) and their results are compared. The dynamic model is developed and simulated in MATLAB/Simulink, and the controller’s performance is evaluated using time-domain metrics such as rise time, set tling time, and steady-state error.Simulation results demonstrate that the GWO based STSMC achieves superior performance, with a settling time of 0.215 s, a rise time of 0.1345 s, and zero steady-state error for pitch angle control. Similarly, for yaw control, it achieves a settling time of 0.2719 s and a rise time of 0.2157 s. The controller maintains stable operation for varying rider masses up to 100 kg, outperforming the PSO based STSMC in both balancing and directional control.The GWO-based STSMC also exhibits faster convergence speed, lower steady-state error, and enhanced robustness against dis turbances compared to the PSO-based controller.These findings highlight the effectiveness of the proposed GWO-optimized STSMC as a robust and efficient control approach for modern self-balancing transporters. The developed controller improves system stability, adaptability, and disturbance rejection, contributing to the advancement of intelligent personal mobility systems for real-world applications. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/8241 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | STSMC controller | |
| dc.subject | Scooter | |
| dc.subject | Grey wolf optimization | |
| dc.subject | and Particle swarm optimization | |
| dc.title | Super Twisted Sliding Mode Controller for Self-balancing Control of a Two-Wheeled Electric Scooter Based on Grey Wolf Optimizer | |
| dc.type | Thesis |