Space Shuttle flying qualities and flight control system assessment study, phase 2

A program of flying qualities experiments as part of the Orbiter Experiments Program (OEX) is defined. Phase 1, published as CR-170391, reviewed flying qualities criteria and shuttle data. The review of applicable experimental and shuttle data to further define the OEX plan is continued. An unconventional feature of this approach is the use of pilot strategy model identification to relate flight and simulator results. Instrumentation, software, and data analysis techniques for pilot model measurements are examined. The relationship between shuttle characteristics and superaugmented aircraft is established. STS flights 1 through 4 are reviewed from the point of view of flying qualities. A preliminary plan for a coordinated program of inflight and simulator research is presented

Bond graph simulation of error propagation in position estimation of a hydraulic cylinder using low cost accelerometers

The indirect calculation from acceleration of transversal displacement of the piston inside the body of a double effect linear hydraulic cylinder during its operating cycle is assessed. Currently an extensive effort exists in the improvement of the mechanical and electronic design of the highly sophisticated MEMS accelerometers. Nevertheless, the predictable presence of measurement errors in the current commercial accelerometers is the main origin of velocity and displacement measurement deviations during integration of the acceleration. A bond graph numerical simulation model of the electromechanical system has been developed in order to forecast the effect of several measurement errors in the use of low cost two axes accelerometers. The level of influence is assessed using quality indicators and visual signal evaluation, for both simulations and experimental results. The obtained displacements results are highly influenced by the diverse dynamic characteristics of each measuring axis. The small measuring errors of a simulated extremely high performance sensor generate only moderate effects in longitudinal displacement but deep deviations in the reconstruction of piston transversal movements. The bias error has been identified as the source of the higher deviations of displacement results; although, its consequences can be easily corrected.Peer ReviewedPostprint (published version

Measurements, Models, Systems and Design

531 s.

Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design

Model-based feedforward control improves tracking performance of motion systems, provided that the model describing the inverse dynamics is of sufficient accuracy. Model sets, such as neural networks (NNs) and physics-guided neural networks (PGNNs) are typically used as flexible parametrizations that enable accurate identification of the inverse system dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics directly. However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance. In order to push performance further, it is therefore crucial to account for noise when performing the identification. To address this problem, this paper proposes the use of a forward system identification using (PG)NNs from noisy data. Afterwards, two methods are proposed for inverting PGNNs to design a feedforward controller for high-precision motion control. The developed methodology is validated on a real-life industrial linear motor, where it showed significant improvements in tracking performance with respect to the direct inverse identification

UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking