Abstract
This work presents the investigation and critical assessment, within the framework of Dynamic Data Driven Applications Systems (DDDAS), of two probabilistic state awareness approaches for fly-by-feel aerial vehicles based on (i) stochastic adaptive time-dependent time series models and (ii) Bayesian learning via homoscedastic and heteroscedastic Gaussian process regression models (GPRMs). Stochastic time-dependent autoregressive (TAR) time series models with adaptive parameters are estimated via a recursive maximum likelihood (RML) scheme and used to represent the dynamic response of a self-sensing composite wing under varying flight states. Bayesian learning based on homoscedastic and heteroscedastic versions of GPRM is assessed via the ability to represent the nonlinear mapping between the flight state and the vibration signal energy of the wing. The experimental assessment is based on a prototype self-sensing UAV wing that is subjected to a series of wind tunnel experiments under multiple flight states.
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Amer, A., Kopsaftopoulos, F.P.: Probabilistic damage quantification via the integration of non-parametric time-series and Gaussian process regression models. In: Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM 2019), pp. 2384–2393, Palo Alto, CA, USA, September 2019. https://doi.org/10.12783/shm2019/32379
Avendaño-Valencia, L.D., Chatzi, E.N., Koo, K.Y., Brownjohn, J.M.: Gaussian process time-series models for structures under operational variability. Front. Built Environ. 3, 69 (2017)
Blasch, E., Ashdown, J., Kopsaftopoulos, F., Varela, C., Newkirk, R.: Dynamic data driven analytics for multi-domain environments. In: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006, p. 1100604. International Society for Optics and Photonics (2019)
Breese, S., Kopsaftopoulos, F., Varela, C.: Towards proving runtime properties of data-driven systems using safety envelopes. In: Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM 2019), pp. 1748–1757, Palo Alto, CA, USA, September 2019. https://doi.org/10.12783/shm2019/32302
Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_86
Goldberg, P.W., Williams, C.K., Bishop, C.M.: Regression with input-dependent noise: a Gaussian process treatment. Adv. Neural Inf. Process. Syst. 10, 1124–1136 (1998)
James, A., Kopsaftopoulos, F.: Data-driven stochastic identification of a UAV under varying flight and structural health states. In: Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM 2019), pp. 1758–1767, Palo Alto, CA, USA, September 2019. https://doi.org/10.12783/shm2019/32303
Kopsaftopoulos, F., Chang, F.-K.: A dynamic data-driven stochastic state-awareness framework for the next generation of bio-inspired fly-by-feel aerospace vehicles. In: Blasch, E., Ravela, S., Aved, A. (eds.) Handbook of Dynamic Data Driven Applications Systems, pp. 697–721. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95504-9_31
Kopsaftopoulos, F., Nardari, R., Li, Y.H., Chang, F.K.: A stochastic global identification framework for aerospace structures operating under varying flight states. Mech. Syst. Signal Process. 98, 425–447 (2018)
Lázaro-Gredilla, M., Titsias, M.K.: Variational heteroscedastic Gaussian process regression. In: Proceedings of the 28th International Conference on Machine Learning, pp. 841–848 (2011)
Ljung, L.: System identification. Wiley encyclopedia of electrical and electronics engineering, pp. 1–19 (1999)
Poulimenos, A., Fassois, S.: Parametric time-domain methods for non-stationary random vibration modelling and analysis – a critical survey and comparison. Mech. Syst. Signal Process. 20(4), 763–816 (2006)
Rasmussen, C.E., Williams, C.K.I. (eds.): Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
Sotiriou, D., Kopsaftopoulos, F., Fassois, S.: An adaptive time-series probabilistic framework for 4-D trajectory conformance monitoring. IEEE Trans. Intell. Transp. Syst. 17(6), 1606–1616 (2016)
Acknowledgment
This work is supported by the U.S. Air Force Office of Scientific Research (AFOSR) grant “Formal Verification of Stochastic State Awareness for Dynamic Data-Driven Intelligent Aerospace Systems” (FA9550-19-1-0054) with Program Officer Dr. Erik Blasch.
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Ahmed, S., Amer, A., Varela, C.A., Kopsaftopoulos, F. (2020). Data-Driven State Awareness for Fly-by-Feel Aerial Vehicles via Adaptive Time Series and Gaussian Process Regression Models. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_9
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