Sensor-based Controller Tuning of Robot Manipulators by Real-time Optimization

For indirect-drive robot manipulators, it is necessary to experimentally tune the controller parameters to compensate for the uncertainties in the actual system and to obtain the desired performance. Since manual tuning of controllers is very time-consuming, much effort has been invested in developing systematic tuning methods. Also, in standard industrial applications of robot manipulators, the performance is evaluated based on load-side information, while the feedback control loop is closed on the motor side. Therefore, the load-side information needs to be considered during controller tuning process. Iterative controller tuning is a method that tunes controllers in a repetitive process using data collected in experiments. In this paper, a model-free method that automatically optimizes an arbitrary multi-variable cost function is used. In the proposed method, the variables are updated while the gradient of the cost function is continuously estimated by a perturbation in real-time. The effectiveness of the controller tuning method is demonstrated by experimental results.