Adaptive fuzzy dynamic surface control for pneumatic muscle systems with full-state constraints and disturbances

In the era of intelligent revolution, pneumatic artificial muscle (PAM) actuators have gained significance in robotics, particularly for tasks demanding high safety and flexibility. Despite their inherent flexibility, PAMs encounter challenges in practical applications because of their complex material properties, including hysteresis, nonlinearity, and low response frequencies, which hinder precise modeling and motion control, limiting their widespread adoption. This study focuses on fuzzy logic dynamic surface control (DSC) for PAMs, addressing full-state constraints and unknown disturbances. We propose an improved neural DSC method, combining enhanced DSC techniques with fuzzy logic system approximation and parameter minimization for PAM systems. The introduction of a novel barrier Lyapunov function during system design effectively resolves full-state constraint issues. A key feature of this control approach is its single online estimation parameter update while maintaining stability characteristics akin to the conventional backstepping method. Importantly, it ensures constraint adherence even in the presence of disturbances. Lyapunov stability analysis confirms signal boundedness within the closed-loop system. Experimental results validate the algorithm’s effectiveness in enhancing control precision and response speed.