  Motion systems are a vital part of many industrial processes. However,
meeting the increasingly stringent demands of these systems, especially
concerning precision and throughput, requires novel control design methods that
can go beyond the capabilities of traditional solutions. Traditional control
methods often struggle with the complexity and position-dependent effects
inherent in modern motion systems, leading to compromises in performance and a
laborious task of controller design. This paper addresses these challenges by
introducing a novel structured feedback control auto-tuning approach for
multiple-input multiple-output (MIMO) motion systems. By leveraging frequency
response function (FRF) estimates and the linear-parameter-varying (LPV)
control framework, the proposed approach automates the controller design, while
providing local stability and performance guarantees. Key innovations include
norm-based magnitude optimization of the sensitivity functions, an automated
stability check through a novel extended factorized Nyquist criterion, a
modular structured MIMO LPV controller parameterization, and a controller
discretization approach which preserves the continuous-time (CT) controller
parameterization. The proposed approach is validated through experiments using
a state-of-the-art moving-magnet planar actuator prototype.
