The minidrone Parrot Mambo® is a promising robotic platform for education control purposes. An important limitation is that its SDK provides sensor data with a maximum nominal frequency of just 2 Hz, creating objective difficulties for feedback control. This paper proposes an observer capable of generating prediction on the data, which allows feeding the controller with a much faster rate than the one allowed by the slow sensor data rate. The predictions are generated by a linear model, whose parameters are identified on-line using a Constrained Kalman Filter. The strategy is successfully validated via extensive experiments with real drones performing altitude stabilisation and trajectory tracking tasks. In particular, the constrained model identification preserves a stable prediction (which is physically meaningful), and hence safe flight, even in the presence of large disturbances.

Constrained kalman filter for adaptive prediction in minidrone flight / Andreetto, M.; Palopoli, L.; Fontanelli, D.. - STAMPA. - 2019-:(2019), pp. 1-6. (Intervento presentato al convegno 2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019 tenutosi a nzl nel 2019) [10.1109/I2MTC.2019.8827131].

Constrained kalman filter for adaptive prediction in minidrone flight

Andreetto M.;Palopoli L.;Fontanelli D.
2019-01-01

Abstract

The minidrone Parrot Mambo® is a promising robotic platform for education control purposes. An important limitation is that its SDK provides sensor data with a maximum nominal frequency of just 2 Hz, creating objective difficulties for feedback control. This paper proposes an observer capable of generating prediction on the data, which allows feeding the controller with a much faster rate than the one allowed by the slow sensor data rate. The predictions are generated by a linear model, whose parameters are identified on-line using a Constrained Kalman Filter. The strategy is successfully validated via extensive experiments with real drones performing altitude stabilisation and trajectory tracking tasks. In particular, the constrained model identification preserves a stable prediction (which is physically meaningful), and hence safe flight, even in the presence of large disturbances.
2019
Conference Record - IEEE Instrumentation and Measurement Technology Conference
NZL
Institute of Electrical and Electronics Engineers Inc.
978-1-5386-3460-8
Andreetto, M.; Palopoli, L.; Fontanelli, D.
Constrained kalman filter for adaptive prediction in minidrone flight / Andreetto, M.; Palopoli, L.; Fontanelli, D.. - STAMPA. - 2019-:(2019), pp. 1-6. (Intervento presentato al convegno 2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019 tenutosi a nzl nel 2019) [10.1109/I2MTC.2019.8827131].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/250921
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact