Neural-optimal tuning of a controller for a parallel robot.

In this article, a double strategy is proposed to find the optimal gains of a cascaded PI controller to minimize the trajectory position error in a five-bar parallel robot. The first strategy employs Differential Evolution to tune constant gains during the execution time of the desired trajectory. Once Differential Evolution achieves convergence on the solution by finding the vector of optimal gains that minimize the position tracking error, all the position error data and current of the two brushless motors are saved. In the second strategy, the data generated in the first strategy is used to train a Deep Neural Network. After that, the trained Deep Neural Network replaces the constant gains of the first strategy with time-varying gains for the desired trajectory. Three working scenarios are proposed to test the generalization of the Deep Neural Network. In the first scenario, a training trajectory is executed. In the second one, a testing trajectory of the Deep Neural Network is evaluated. In the third one, a mass change is generated in the middle of the cycle. The results show that the Deep Neural Network is robust to different trajectories and mass changes during the execution of pick and place tasks. [ABSTRACT FROM AUTHOR]