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BLDC of Robotic Manipulators with Neural Torque Compensator based Optimal Robust Control

This paper presents a neural torque compensator for robotic manipulator control based on optimal robust control in the presence of nonlinear uncertain unknown load in system parameters. The first section introduces mathematical model of robotic manipulator electric driver with nonlinear parameters. In the next section, the structures of the neural compensated torque based on optimal robust control Theta-D are analyzed. The quality criterion in control system is analyzed with the combination of nonlinear optimal compensated torque based on linear quadratic Gaussian algorithm and neural torque compensator. Optimal proportional-integral regulators are developed in speed loop and position loop of manipulator control system, which improve dynamic characteristics.