Intelligent Optimal Feed-Back Torque Control of a 6DOF Surgical Rotary Robot

Sophisticated surgeons are widely indicating the use of surgical robots in order to reject human error, increase precision, and speed. Among well-known robotic mechanisms, parallel robots are broadly more investigated regarding their special characters as higher acceleration, speed, and accuracy, and less weight. Specific surgical procedures confine, and restrict their workspace, while controlling and validating the robots are complicated regarding to their complex dynamic. To this end, in this paper, a 6-DOF robot, with rotary manipulators, is designed and controlled. Addressing nonlinearity of parallel robots, an innovative methodology is formulated to robustly penalize the error of tracking at end effector through a Linear Quadratic Integral (LQI) regulator with online Artificial Neural Network (ANN) gain tuning, based on non-linear model in format of a Linear Time Invariant (LTI) model. As validation, the controller is implemented using MATLAB on the non-linear model designed in Adams software online. Simulation results demonstrate the optimal controller penalizing the error while minimizing torque on each of the rotary manipulator. In addition, the method defines the workspace of both the states and torques, which is an introduction to comprehensive design of such robots.