Space shuttle flying qualities and criteria assessment

Work accomplished under a series of study tasks for the Flying Qualities and Flight Control Systems Design Criteria Experiment (OFQ) of the Shuttle Orbiter Experiments Program (OEX) is summarized. The tasks involved review of applicability of existing flying quality and flight control system specification and criteria for the Shuttle; identification of potentially crucial flying quality deficiencies; dynamic modeling of the Shuttle Orbiter pilot/vehicle system in the terminal flight phases; devising a nonintrusive experimental program for extraction and identification of vehicle dynamics, pilot control strategy, and approach and landing performance metrics, and preparation of an OEX approach to produce a data archive and optimize use of the data to develop flying qualities for future space shuttle craft in general. Analytic modeling of the Orbiter's unconventional closed-loop dynamics in landing, modeling pilot control strategies, verification of vehicle dynamics and pilot control strategy from flight data, review of various existent or proposed aircraft flying quality parameters and criteria in comparison with the unique dynamic characteristics and control aspects of the Shuttle in landing; and finally a summary of conclusions and recommendations for developing flying quality criteria and design guides for future Shuttle craft

Study of Two Instrumental Variable Methods for Closed-Loop Multivariable System Identification.

New control strategies are based on the model of the process and it is thus necessary to identify the systems to be controlled. It is also often necessary to identify them during closed-loop operation in order to maintain efficient operation and product quality. Some results of multivariable closed-loop identification carried out on a simulated 2 x 2 linear time-invariant system, using two new versions of instrumental variable methods called IV4D and IV4UP as the identification methods, are presented. In each case pseudorandom binary signals (PRBS), or dithers, are applied to the outputs of the feedback controllers. Algorithms IV4D and IV4UP are created in a four step environment where iterations are performed to obtain the best possible estimated model. For IV4D only the dither is used as part of the instrument. For IV4UP only the part of the input that comes from the dither is used for the instrument. This is obtained with the estimated model and with the description of the controllers using the closed-loop transfer function between the dither and the input to the process. The implementation is made to be run in MatLab and it uses several of the functions defined in its System Identification Toolbox (Ljung, 1991). Both instrumental variable (IV) algorithms perform very well identifying closed-loop multivariable systems under the influence of white noise and correlated noise disturbances. The two new instrumental variable methods are compared with the prediction error method, PEM, and with IV4, the regular instrumental variable open-loop algorithm, both of them are obtained from the MatLab System Identification Toolbox. IV4 does not perform well in closed-loop operation. From the simulated results, the performances of the new IV algorithms are the best but, PEM\u27s performance is very close. Finally, real plant data are analyzed with IV4D and its results are compared with the results of other identification methods, PEM and Dynamic Matrix Identification (DMI) (Cutler and Yocum, 1991). For this closed-loop real plant data PEM is the best that performs followed by IV4D, while DMI does not perform well

Multiobjective Identification of a Feedback Synthetic Gene Circuit

© 2020 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Kinetic (i.e., dynamic) semimechanistic models based on the first principles are particularly important in systems and synthetic biology since they can explain and predict the functional behavior that emerges from the time-varying concentrations in cellular components. However, gene circuit models are nonlinear higher order ones and have a large number of parameters. In addition, experimental measurements are often scarce, and enough signal excitability for identification cannot always be achieved. These characteristics render the identification problem ill-posed, so most gene circuit models present incomplete parameter identifiability. Thus, parameter identification of typical biological models still appears as an open problem, where ensemble modeling approaches and multiobjective optimization arise as natural options. We address the problem of identifying the stochastic model of a closed-loop synthetic genetic circuit designed to minimize the gene expression noise. The model results from the feedback interaction between two subsystems. Besides incomplete parameter identifiability, the closed-loop dynamics cannot be directly identified due to the lack of enough input signal excitability. We apply a two-stage approach. First, the open-loop averaged time-course experimental data are used to identify a reduced-order stochastic model of the system direct chain. Then, closed-loop steady-state stochastic distributions are used to identify the remaining parameters in the feedback configuration. In both cases, multiobjective optimization is used to address the parameter identifiability, providing sets of parameters valid for different state-space regions. The methodology gives good identification results, provides clear guidelines on the effect of the parameters under different scenarios, and it is particularly useful for easily combining time-course population averaged and steady-state single-cell distribution experimental data.This work was supported by the European Union and Spanish Government, MINECO/AEI/FEDER under Grant DPI2017-82896-C2-1-R. The work of Y. Boada was supported by the Universitat Politecnica de Valencia under Grant FPI/2013-3242.Boada-Acosta, YF.; Vignoni, A.; Picó, J. (2020). Multiobjective Identification of a Feedback Synthetic Gene Circuit. IEEE Transactions on Control Systems Technology. 28(1):208-223. https://doi.org/10.1109/TCST.2018.2885694S20822328

Closed-loop Identification of an Industrial Extrusion Process

This paper deals with the challenging problem of closed-loop identification for multivariable chemical processes and particularly the estimation of an open-loop plant model for a lab-scale industrial twin-screw extruder used in a powder coatings manufacturing line. The aim is to produce a low order efficient model in order to assist the scaling-up and the model-based control design of the manufacturing process. To achieve this goal, a two-stage indirect approach has been deployed which relies on the a-priori knowledge of the controller parameters in order to extract good estimates of the open-loop dynamics of the underlying process. As input excitation signals we have used multiple single variable step tests at various operating conditions (current industrial practice) carried out manually in order to generate the data-set which captures the dynamics of the extrusion process. In order to increase the efforts for obtaining a suitable plant model, we have employed various identification techniques, such as Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) in order to generate candidate closed-loop models that fit to the original input-output process data. Then, a comparison of the estimated models was performed by means of the mean square error and data fitting criteria in order to select the model that best describes the dynamic behaviour of the extrusion process. Model validation based on closed-loop step responses also used as verification of the results