Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System
This work addresses the practical implementation gap for ADP in control systems, offering a model-free, data-driven solution for trajectory tracking, though it is incremental as it builds on existing ADP theory.
The paper tackles the scarcity of practical applications for Adaptive Dynamic Programming (ADP) by designing an ADP-based optimal trajectory tracking controller for a large-scale ball-on-plate system, resulting in significantly reduced control costs compared to setpoint controllers and eliminating the need for system models or manual tuning.
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.