Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System

TL;DR

This paper presents an adaptive dynamic programming-based control method for a large-scale ball-on-plate system, demonstrating improved tracking and reduced control costs without needing system models or manual tuning.

Contribution

It introduces a novel ADP-based optimal trajectory tracking controller that uses an approximated reference trajectory and requires minimal measured data for training.

Findings

Significantly reduces control cost compared to setpoint controllers.

Requires only a small amount of measured data for training.

Outperforms model-based controllers by eliminating the need for system models and manual tuning.

Abstract

While many theoretical works concerning Adaptive Dynamic Programming (ADP) have been proposed, application results are scarce. Therefore, we design an ADP-based optimal trajectory tracking controller and apply it to a large-scale ball-on-plate system. Our proposed method incorporates an approximated reference trajectory instead of using setpoint tracking and allows to automatically compensate for constant offset terms. Due to the off-policy characteristics of the algorithm, the method requires only a small amount of measured data to train the controller. Our experimental results show that this tracking mechanism significantly reduces the control cost compared to setpoint controllers. Furthermore, a comparison with a model-based optimal controller highlights the benefits of our model-free data-based ADP tracking controller, where no system model and manual tuning are required but the controller is tuned automatically using measured data.