Neural network adaptive force control for pneumatic polishing end-actuator with external disturbances and full-state constrains

In pneumatic polishing, the nonlinear, time-varying, and uncertain contact characteristics introduce significant modeling inaccuracies, posing substantial challenges to the realization of precise and robust force control. This paper proposes a neural-network adaptive force control strategy for a pneumatic polishing end-actuator under external disturbances and full-state constraints. To estimate the unmeasurable states and enhance the ant disturbance capability, a composite observer is developed to estimate the internal states and external disturbances in real time. Under the adaptive backstepping design framework, a radial-basis-function–neural-network–based adaptive learning mechanism is employed to approximate the nonlinear uncertainties, and a dynamic surface-control structure is introduced to avoid the complexity explosion in conventional recursive designs. Furthermore, a barrier Lyapunov function is integrated to ensure compliance with the full-state constraints throughout the control process. The convergence of the controller is verified through stability analyses, and the effectiveness and superiority of the control scheme is verified via experiments in four different polishing scenarios. The results show that the proposed control method achieves an average force tracking error less than 0.07 N and convergence time less than 2.35 s, showing higher control accuracy, faster transient response, and stronger robustness, than similar control algorithms.