Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology an optimal torque for 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, linear quadratic integral regulator least sensitivity to controller parameter choices is designed, associated online artificial neural network gain tuning. The nonlinear system implemented in ADAMS, formulated MATLAB minimize real-time robustly. To validate performance, ADAMS are linked together performance on simulated validated as real time. Practically, robot fabricated implemented. method assessed by simulation experiments, exhibiting viability developed highlighting improvement 45% averagely, from optimum zero-error convergence points view. Consequently, experiment results allow demonstrating robustness method, presence motor saturation, uncertainties, unknown disturbances such intrinsic properties test bed.