Neural approximation of fuzzy gain-scheduled PID control for a dual-crank strandbeest walking mechanism

This study presents a simulation-based comparison of PID, Fuzzy-PID, and artificial neural network (ANN)-assisted gain-scheduled PID controllers for a modular dual-motor Strandbeest walking mechanism. The mechanism was designed in SolidWorks and transferred to MATLAB/Simscape Multibody, where each leg group is driven by an independent crank and the crank angle is controlled through a joint torque actuator. The ANN is not used as a stand-alone controller; instead, it provides a smooth approximation of the fuzzy gain-scheduling map used in the Fuzzy-PID controller. The controllers are evaluated under step, sinusoidal, disturbance-rejection, and square-wave reference scenarios. The ANN approximated the fuzzy gain map with coefficients of determination of 0.99, 0.92, and 0.83 for the proportional, integral, and derivative gains, respectively. The ANN-assisted controller reduced tracking error in the sinusoidal, square-wave, and disturbance cases, while the Fuzzy-PID controller achieved the lowest stepresponse overshoot. The results show that neural approximation can provide a computationally simple and smooth gain-scheduling structure for simulation-based crank-angle control, although it should not be interpreted as an exact closed-loop reproduction of the original Fuzzy-PID controller.