Enhancing Trajectory Tracking Accuracy in Three Wheeled Mobile Robots using Backstepping Fuzzy Sliding Mode Control
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Date
2023-10
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Addis Ababa University
Abstract
The rise in robotics technology has led to increased interest in three-wheeled mobile robots
(TWMRs) due to their agility and adaptability across various applications. However, effectively
controlling TWMRs presents a significant challenge owing to their inherent nonholonomic
constraint, which restricts their independent movement in all directions. Additionally,
factors like sensor noise, nonlinear system dynamics, and uncertain system parameters add
to the complexity controlling of TWMRs. This research endeavors to enhance the precision
of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode
Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO),
coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts
a comprehensive performance comparison between BFSMC and BSMC across various trajectory
patterns, revealing substantial improvements in trajectory tracking accuracy with
BFSMC. BFSMC demonstrates improved performance compared to BSMC across various
trajectory types, quantified by calculating the percentage improvement in trajectory tracking
using Integral Absolute Error (IAE). Specifically, it achieves a 51.97% improvement for
circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement
for spiral trajectories.. Moreover, BFSMC demonstrates superior robustness in
the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared
to BSMC. The integration of the Extended Kalman Filter further improve accuracy,
particularly in noisy conditions. Simulation results conducted using MATLAB/Simulink
software validate the effectiveness of this approach in achieving superior trajectory tracking
accuracy in TWMRs.
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Keywords
Trajectory Tracking, EKF, BFSMC, BSMC, TWMR, PSO, Nonholonomic Constraint, Disturbances, Sensor Noise