In this project data-driven approaches, which by-pass the system modelling step and so do not suffer from unmodelled dynamics, are being investigated to improve the tracking performance of systems subject to stochastic disturbances.
For the general tracking problem, a precompensator controller is used to filter the desired output signal before it is applied as an input to the system. The precompensator’s parameters are tuned directly using measured data. This data is affected by stochastic disturbances, such as measurement noise. The effect of these disturbances on the calculated parameters is being studied and the correlation approach is used to reduce it.
For the specific problem where the tracking task is repetitive, a situation frequently encountered in industrial applications, Iterative Learning Control (ILC) is proposed. ILC uses measurements from previous repetitions to adjust the system’s input for the current repetition in a manner so as to improve the tracking. As measurements are used, the calculated input is sensitive to the stochastic disturbances affecting them. The effect of these disturbances on the learning procedure is being examined and algorithms that are less sensitive to their presence are being developed.
Additionally, the proposed methods are being extended for use on Linear Parameter Varying (LPV) systems, in which the system’s dynamics change as a function of a scheduling parameter.