Fuzzy Iterative Learning Contouring Control

Iterative learning contouring control (ILCC) improves contouring accuracy in multi-axis motion systems via the equivalent contour error formulation. However, its convergence strongly depends on the learning gain. Large gains may induce overly aggressive updates and local divergence, degrading performance, whereas small gains lead to slow convergence. Moreover, contour error convergence is typically non-uniform along the trajectory, and local divergence may still occur despite global convergence, particularly near error saturation regions. To address these issues, a fuzzy inference mechanism is integrated into the online ILCC framework, yielding an online ILCC with fuzzy-regulated convergence parameters (online ILCCf), enabling adaptive regulation of the learning gain. Two regulation strategies are developed: (i) online ILCCf${}_{\mathrm{i}}$, an independent multi-parameter regulation scheme; and (ii) online ILCCf${}_{\mathrm{u}}$, a unified single-parameter regulation scheme. The fuzzy mechanism adaptively adjusts the convergence parameters online according to the instantaneous magnitude of the equivalent contour error. Experimental results on a six-axis industrial robot demonstrate fast convergence while maintaining satisfactory contouring performance. Among all comparison cases , online ILCCf${}_{\mathrm{i}}$ achieves the best performance, reducing the RMS position error from $7.26\times {10}^{-1}$ mm to $5.93\times {10}^{-2}$ mm and the RMS orientation error from $6.95\times {10}^{-4}$ rad to $5.64\times {10}^{-5}$ rad, without oscillation or local divergence. Further simulations confirm robustness under model uncertainty and measurement noise.