Validation of a Stewart platform inspection system with an artificial neural network controller

Abstract

The use of thermographic cameras attached to Stewart platforms may become a valuable approach to build non-destructive inspection systems. However, achieving successful inspections requires positioning the platform that holds the camera very accurately, especially when the camera lacks of autofocus capabilities. Thus, if the distance from the lenses to the inspection surface is in range, sharp quality images may be obtained, otherwise, images may suffer defocusing producing poor inspection results. This work presents a new Artificial Neural Network (ANN) control aimed at improving the accuracy of the Stewart platform that holds a camera, hence improving the quality of the inspection system. The proposed ANN architecture positions the platform more accurately than the vendor provided Proportional Integral (PI) control strategy. The system kinematics and dynamics are analyzed in order to design the ANN model for the system. The developed control scheme is based on the inverse ANN model with an additional integral control. The advantages of this novel approach are that (1) the controller considers the interactions among the actuators and (2) includes a training process that continuously fine-tunes the ANN weight matrix. The proposed control scheme was validated experimentally, and the performance compared with tuned PI control, taken as the reference, for two different inspection trajectories. The results show that the ANN control is much more accurate and efficient than the reference PI (above 40% error reduction in the actuator positioning with the same power consumption), hence performing the inspections successfully.