Abstract—Electric wheelchairs are essential for individuals with lower motor disabilities, but joystick operation remains challenging for users with cerebral palsy. This study introduces Feedback Control using Auto-Regressive Moving Average (ARMA) to Control Wheelchairs (FCWCARMA), a novel approach designed to enhance joystick maneuverability. Compared to the Model Reference Adaptive Control with Elephant Herding Optimization (MRAC-EHO) model, which utilizes model-referenced adaptive control with Elephant Herding Optimization, FCWCARMA employs an Autoregressive Moving Average (ARMA) model with least square regression for real-time parameter estimation. By continuously adjusting the joystick input based on speed deviations from optimal terrain-specific values, FCWCARMA improves wheelchair control. Simulation results demonstrate that FCWCARMA achieves a 9% faster response time in reaching the desired speed compared to MRAC-EHO, highlighting its superior performance.

Keywords—wheelchair control, Autoregressive Moving Average (ARMA), feedback control, motor disabilities, assistive technologies, empirical models

Cite: Chadi F. Riman and Wael H. F. Aly, "Feedback Control Using ARMA to Maneuver Wheelchairs," International Journal of Mechanical Engineering and Robotics Research, Vol. 14, No. 3, pp. 299-308, 2025. doi: 10.18178/ijmerr.14.3.299-308

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