Comparison of different repetitive control architectures: synthesis and comparison. Application to VSI Converters

Repetitive control is one of the most used control approaches to deal with periodic references/disturbances. It owes its properties to the inclusion of an internal model in the controller that corresponds to a periodic signal generator. However, there exist many different ways to include this internal model. This work presents a description of the different schemes by means of which repetitive control can be implemented. A complete analytic analysis and comparison is performed together with controller synthesis guidance. The voltage source inverter controller experimental results are included to illustrative conceptual developmentsPeer ReviewedPostprint (published version

FVF-Based Low-Dropout Voltage Regulator with Fast Charging/Discharging Paths for Fast Line and Load Regulation

A new internally compensated low drop-out voltage regulator based on the cascoded flipped voltage follower is presented in this paper. Adaptive biasing current and fast charging/discharging paths have been added to rapidly charge and discharge the parasitic capacitance of the pass transistor gate, thus improving the transient response. The proposed regulator was designed with standard 65-nm CMOS technology. Measurements show load and line regulations of 433.80 μV/mA and 5.61 mV/V, respectively. Furthermore, the output voltage spikes are kept under 76 mV for 0.1 mA to 100 mA load variations and 0.9 V to 1.2 V line variations with rise and fall times of 1 μs. The total current consumption is 17.88 μA (for a 0.9 V supply voltage).Ministerio de Economía y Competitividad TEC2015-71072-C3-3-RConsejería de Economía, Innovación y Ciencia. Junta de Andalucía P12-TIC-186

Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

The Robo-AO-2 facility for rapid visible/near-infrared AO imaging and the demonstration of hybrid techniques

We are building a next-generation laser adaptive optics system, Robo-AO-2, for the UH 2.2-m telescope that will deliver robotic, diffraction-limited observations at visible and near-infrared wavelengths in unprecedented numbers. The superior Maunakea observing site, expanded spectral range and rapid response to high-priority events represent a significant advance over the prototype. Robo-AO-2 will include a new reconfigurable natural guide star sensor for exquisite wavefront correction on bright targets and the demonstration of potentially transformative hybrid AO techniques that promise to extend the faintness limit on current and future exoplanet adaptive optics systems.Comment: 15 page