Robust and fast schemes in broadband active noise and vibration control

This thesis presents robust and fast active control algorithms for the suppression of broadband noise and vibration disturbances. Noise disturbances, e.g., generated by engines in airplanes and cars or by air ow, can be reduced by means of passive or active methods

Extremely fast focal-plane wavefront sensing for extreme adaptive optics

We present a promising approach to the extremely fast sensing and correction of small wavefront errors in adaptive optics systems. As our algorithm's computational complexity is roughly proportional to the number of actuators, it is particularly suitable to systems with 10,000 to 100,000 actuators. Our approach is based on sequential phase diversity and simple relations between the point-spread function and the wavefront error in the case of small aberrations. The particular choice of phase diversity, introduced by the deformable mirror itself, minimizes the wavefront error as well as the computational complexity. The method is well suited for high-contrast astronomical imaging of point sources such as the direct detection and characterization of exoplanets around stars, and it works even in the presence of a coronagraph that suppresses the diffraction pattern. The accompanying paper in these proceedings by Korkiakoski et al. describes the performance of the algorithm using numerical simulations and laboratory tests.Comment: SPIE Paper 8447-7

Leren van Mobiliteits-Experimenten tot de Vierde Macht:: een Meta-Lab tussen vier Nederlandse Stedelijke Regio’s

Dit paper presenteert de voorlopige resultaten van het project SUMMALab waarin een nieuwe ‘meta-lab’ benadering wordt getest. SUMMALab is een meta-lab rond mobiliteits-experimenten in de Metropoolregio Amsterdam, de Metropoolregio Rotterdam-Den Haag en de gemeenten Den Haag, Delft en Rotterdam. Een meta-lab is nietzelf een lab, maar een verzamelplaats waar verschillende onderzoeken en experimenten in samenhang worden gebracht zodat er sneller en beter van de experimenten geleerd kan worden. De meta-lab benadering respecteert en ondersteunt enerzijds lokale leeragenda's en hun focus op lokale oplossingen voor lokale problemen, en gebruikt anderzijds het potentieel van lokale experimenten om bij te dragen aan een centrale leeragenda gebaseerd op de 'grote maatschappelijke uitdagingen'

Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots

The estimation of the pose of a differential drive mobile robot from noisy odometer, compass and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment. https://doi.org/10.4018/IJAIML.2020010101 https://www.linkedin.com/in/john-bolte-0856134

A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems

The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method is demonstrated by simulation experiments

A fast-array Kalman filter solution to active noise control

A Kalman filter solution to active control and its fast-array implementation are provided. The adaptive control problem is formulated as a state-estimation problem and no interchanging of the adaptive filter and the secondary-path is imposed. Moreover, no estimate of the disturbance signal is needed, and we exploit the structure in the state–space matrices to derive a fast-array implementation. A minimum variance estimate of the controller coefficients and the secondary path state is obtained. When there is no uncertainty in the secondary path, state equivalence with the modified filtered-RLS algorithm is proven. Using exponential forgetting, the analysis shows that in the generation of the filtered reference signal in the modified filtered-RLS, exponential forgetting should be incorporated too. Simulations show the superiority in convergence of the fast-array Kalman algorithm over the fast-array modified filtered-RLS algorith

Experimental validation of optimization concepts for focal-plane image processing with adaptive optics

We show experimental results demonstrating the feasibility of an extremely fast sequential phase-diversity (SPD) algorithm for point sources. The algorithm can be implemented on a typical adaptive optics (AO) system to improve the wavefront reconstruction beyond the capabilities of a wavefront sensor by using the information from the imaging camera. The algorithm is based on a small-phase approximation enabling fast numerical implementation, and it finds the optimal wavefront correction by iteratively updating the deformable mirror. Our experiments were made at an AO-setup with a 37 actuator membrane mirror, and the results show that the algorithm finds an optimal image quality in 5-10 iterations, when the initial wavefront errors are typical non-common path aberrations having a magnitude of 1-1.5 rad rms. The results are in excellent agreement with corresponding numerical simulations

Sensor fusion of odometry and a single beacon distance measurement

\u3cp\u3eThe pose estimation of a differential drive robot from noisy odometry and a noisy beacon distance measurement is studied. It is shown that the problem is a state estimation problem with unknown input, which under some assumptions regarding the noise on the state, can be rewritten in a state estimation problem. An heuristic sensor fusion algorithm is proposed and compared with the extended Kalman filter and the particle filter in a simulation experiment.\u3c/p\u3