Due to advantages such as a high power-to-weight ratio, a simple structure, and low cost, pneumatic systems are widely applied in automation. However, precise position control of pneumatic actuators is challenging because of factors such as friction, compressibility, and external disturbances. This paper presents a backstepping sliding mode control (BSMC) strategy based on the extended state observer (ESO) for pneumatic cylinder position tracking. A nonlinear model of the pneumatic system is first established, then system states and disturbances are estimated by an ESO, next the BSMC approach is developed using backstepping method and sliding mode control theory, and the stability of the ESO and controller is analyzed using Lyapunov theory. Finally, simulations and experiments on a pneumatic testbed are performed to compare the effectiveness of the proposed approach with PID control. The results show that the proposed strategy improves tracking accuracy and robustness against disturbances, with a 77.04% reduction in root mean square error (RMSE). This research provides a promising control solution for automated pneumatic polishing robots.

Robotic polishing is crucial for achieving superior surface finishes in manufacturing. However, precise force control presents significant challenges, particularly for curved workpieces exhibiting time-varying stiffness. Traditional methods typically struggle to adapt to these dynamic conditions, often leading to inconsistent results and suboptimal surface quality. This study proposes an Adaptive Impedance Control based on Visual Guidance (AICVG) strategy for robotic polishing. This approach integrates real-time visual feedback for geometric perception and adaptive tool path generation with a fuzzy logic system that dynamically adjusts impedance parameters to account for unforeseen surface stiffness variations. Simulations and experimental validations conducted on a robotic platform demonstrate that the AICVG strategy significantly outperforms both traditional impedance control and conventional fuzzy logic-based adaptive impedance control. Specifically, it maintains force control errors within ±1.5 N under dynamic stiffness conditions and achieves a 60% reduction in workpiece surface roughness compared to the aforementioned alternative methods. This study presents a robust and precise control framework that significantly enhances the adaptability and efficacy of robotic polishing for complex geometries, thereby advancing automated solutions in high-precision manufacturing.