article · Engineering Research Express
Three-wheeled mobile robots offer agility across diverse settings, but their nonholonomic constraints, sensor noise, and nonlinear dynamics make accurate motion control difficult. This research develops a control method to improve trajectory tracking precision for such robots. The approach combines backstepping fuzzy sliding mode control with parameter optimisation via particle swarm optimisation, alongside an extended Kalman filter to estimate states accurately in noisy conditions. In comparative testing against standard backstepping sliding mode control across various path shapes, the proposed method showed substantial reductions in tracking error. Specifically, the integral time absolute error decreased by 51.97 percent for circular paths, 82.09 percent for figure-of-eight patterns, and 84.073 percent for spiral paths. The controller also demonstrates improved robustness against disturbances, noise, parameter variations, and unmodelled dynamics.
Mobile robots must follow precise paths to operate effectively in complex environments. However, physical constraints and sensor noise often degrade their steering accuracy. By substantially reducing tracking errors and remaining reliable despite unpredictable disturbances, this control design helps ensure that automated wheeled vehicles can maintain stable and accurate navigation even when operating in noisy, dynamic settings.
The method could be used by robotic system designers and control engineers working on automated wheeled platforms. While the abstract demonstrates performance improvements across complex trajectories and noisy conditions, the work remains at an early algorithmic development and simulation stage. Moving towards commercial use would require validation on physical three-wheeled platforms in practical, real-world deployment environments.
AI-generated from the published abstract. Always read the original work before citing.
Abstract The rise in robotics technology has increased interest in ThreeWheeled 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 constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling 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 Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time 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. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.
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DOI: 10.1088/2631-8695/ad79b9
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