Abstract
A new data-driven iterative learning control methodology is presented which uses the frequency response data of a system in order to avoid the problem of unmodelled dynamics associated with low-order parametric models. A convex optimisation problem is formulated to design the learning filters such that the convergence criterion is minimised. Since the frequency response data of the system is used in obtaining these filters, robustness is ensured by eliminating the uncertainty in the modelling process. The effectiveness of the method is illustrated by considering a case study where the proposed design scheme is applied to a power converter control system for a specific accelerator requirement at CERN.
Highlights
The increasing performance demands of today's modern systems have created challenging tasks for the control systems engineer
To simplify the controller design process, these systems are approximated with low-order models
A frequency-domain approach has been used in order to avoid the problem of unmodelled dynamics associated with parametric models
Summary
The increasing performance demands of today's modern systems have created challenging tasks for the control systems engineer. To simplify the controller design process, these systems are approximated with low-order models (which reduces the effort required to properly synthesise a controller). This approximation, can lead to stability and performance problems since these low-order models are subject to model uncertainty. A survey on the differences associated with model-based control and data-driven control has been addressed in [1, 2]. For linear time-invariant systems, the parametric uncertainties and the unmodelled dynamics associated with the data-driven scheme are irrelevant, and the only source of uncertainty is the measurement process. In addition to the problem of unmodeled dynamics is the issue of tracking in high performance systems. Feedback control suffers from a lag in transient tracking since the controller has to react to inputs and disturbances
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