  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.
