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

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 based on their complex dynamic. To this end, in this paper, a 6-DOF robot, with linear manipulators, is designed and controlled. Addressing the inherent nonlinearity of the robot mechanism, an adaptive PID manipulator is employed and validated with nonlinear model. Thus, this paper aims to implement the controller using MATLAB on the nonlinear model designed in Adams software as online. Furthermore, as feasibility study, a 3-axis gyro sensor is calibrated and used to produce complex real movements and desires, which is sending real time signals to MATLAB with microcontroller ATmega 2560. Simulation results show that the adaptive controller identifies the system's dynamic and proceed to reducing error of the position by the time. In addition, the method defines the workspace of both the states and forces which can be an introduction to comprehensive design of such robots.

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.