Learning for Advanced Motion Control

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced Motion Control, 202

On the robustness of the average power ratios in damping estimation: application in the structural health monitoring of composites beams

In composites structures, cracking, delamination will cause changes in the measured dynamic response of structure and so on experimentally modal parameters. Estimation of damping in structural control often poses a difficult problem especially using broadband experiments. If these estimations are faulty, it is difficult to propose a robust Structural Health Monitoring (SHM) algorithm. Recently H.P. Yin introduced the optimal power ratios damping estimator. A new theoretical basis of the bandwidth method for the damping estimation from frequency response functions (in case of a single degree of freedom system) has been proposed. The main goal of this paper is to study the robustness of this enhanced damping estimator on simulated signal (sampling frequency, Signal to Noise Ratio and damping level/density), and also compare its performance with industrial improved estimator like “Polymax” on experimental Frequency Response Functions (FRFs). The pole shifts would be studied as a change in the frequency-damping plane function of level and density of damage

Parametric macromodeling of lossy and dispersive multiconductor transmission lines

We propose an innovative parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs) that can be used for interconnect modeling. It is based on a recently developed method for the analysis of lossy and dispersive MTLs extended by using the multivariate orthonormal vector fitting (MOVF) technique to build parametric macromodels in a rational form. They take into account design parameters, such as geometrical layout or substrate features, in addition to frequency. The presented technique is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels allow to perform design space exploration, design optimization, and sensitivity analysis efficiently. Numerical examples validate the proposed approach in both frequency and time domain

Exploring, tailoring, and traversing the solution landscape of a phase-shaped CARS process

Pulse shaping techniques are used to improve the selectivity of broadband CARS experiments, and to reject the overwhelming background. Knowledge about the fitness landscape and the capability of tailoring it is crucial for both fundamental insight and performing an efficient optimization of phase shapes. We use an evolutionary algorithm to find the optimal spectral phase of the broadband pump and probe beams in a background-suppressed shaped CARS process. We then investigate the shapes, symmetries, and topologies of the landscape contour lines around the optimal solution and also around the point corresponding to zero phase. We demonstrate the significance of the employed phase bases in achieving convex contour lines, suppressed local optima, and high optimization fitness with a few (and even a single) optimization parameter