This study compares three different control algorithms for a muscle-like actuated arm developed to replicate motion in two degrees-of-freedom (dt): elbow flexion/extension (f/e) and forearm pronationination (ps). Electromyogram (EMG) is employed to help determine the control signal used to actuate the muscle cylinders. Three different types of control strategies were attempted. The first algorithm used fuzzy logic with EMG signals and position error as control inputs (Fuzzy Controller). The second algorithm incorporated moment arm information into the existing fuzzy logic controller (Fuzzy-MA Controller). The third algorithm was a conventional Proportional-Integral-Derivative (PID) controller, which operated solely on position and integration error (PID Controller). Overall, moment arm scaling aided the fuzzy logic control algorithm by improving movement accuracy as determined by relative error and correlation. The PID controller resulted in the most accurate movement tracking after fine tuning the control gains. This study implies that moment arm scaling is an effective tool for improving motion tracking accuracy of the fuzzy controller in the mechanical arm. The study also implies that PID controller can be used as a substitute for the fuzzy based controller once the desired motion is prescribed.
