State Estimation for a Small Scale Flybar-less Helicopter

Al-Sharman, Mohammad; Abdel–Hafez, Mamoun F.; Al-Omari, Muhannad

doi:10.1016/j.protcy.2014.09.079

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data consistency checks were carried out in time domain, using flight path reconstruction (FPR) for the data gathered from flight tests (Nusrath et al , 2014; Al-Sharman et al , 2014). FPR is a procedure used to estimate the systematic errors normally present in flight-measured data.…”

Section: Test Vehicle and Data Gatheringmentioning

confidence: 99%

System identification of flybar-less rotorcraft UAV

Nusrath

Lulu

Singh

2020

AEAT

View full text Add to dashboard Cite

Purpose This paper aims to build an accurate mathematical model which is necessary for control design and attitude estimation of a miniature unmanned rotorcraft and its subsequent conversion to an autonomous vehicle. Design/methodology/approach Frequency-domain system identification of a small-size flybar-less remote controlled helicopter is carried out based on the input–output data collected from flight tests of the instrumented vehicle. A complete six degrees of freedom quasi-steady dynamic model is derived for hover and cruise flight conditions. Findings The veracity of the developed model is ascertained by comparing the predicted model responses to the actual responses from flight experiments and from statistical measures. Dynamic stability analysis of the vehicle is carried out using eigenvalues and eigenvectors. The identified model represents the vehicle dynamics very well in the frequency range of interest. Research limitations/implications The model needs to be augmented with additional terms to represent the high-frequency dynamics of the vehicle. Practical implications Control algorithms developed using the first principles model can be easily reconfigured using the identified model, because the model structure is not altered during identification. Originality/value This paper gives a practical solution for model identification and stability analysis of a small-scale flybar-less helicopter. The estimated model can be easily used in developing control algorithms.

show abstract

Section: Test Vehicle and Data Gatheringmentioning

confidence: 99%

System identification of flybar-less rotorcraft UAV

Nusrath

Lulu

Singh

2020

AEAT

View full text Add to dashboard Cite

show abstract

“…Another advantage of such an FBL rotor is that the inputs from the swashpate go directly to the main rotor blades for a fast and direct response [13]. Therefore, for many of the latest small-scale helicopters with higher maneuverability, the stabilizer flybar has been replaced by the electronic stabilization systems [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Comprehensive Kinematic and Inverse Dynamic Model for the Flybar-Less Swashplate Mechanism: Application on a Small-Scale Unmanned Helicopter

et al. 2020

View full text Add to dashboard Cite

In this paper, we propose a novel kinematic and inverse dynamic model for the flybar-less (FBL) swashplate mechanism of a small-scale unmanned helicopter. The swashplate mechanism is an essential configuration of helicopter flight control systems. It is a complex, multi-loop chain mechanism that controls the main rotor. In recent years, the demand for compact swashplate designs has increased owing to the development of small-scale helicopters. The swashplate mechanism proposed in this paper is the latest architectures used for hingeless rotors without a Bell-Hiller mixer. Firstly, the kinematic analysis is derived from the parallel manipulators concepts. Then, based on the principle of virtual work, a methodology for deriving a closed-form dynamic equation of the FBL swashplate mechanism is developed. Finally, the correctness and efficiency of the presented analytical model are demonstrated by numerical examples and the influence factors of the loads acted on actuators are discussed.

show abstract

“…For example, in [17], the readings of the kinematic OF model are fused with the measurements of the GPS/INS to estimate the 3D velocity states and position of an object. In [18,19,16], a high-Precision Landing Using an Adaptive Fuzzy Multi-Sensor Data Fusion Architecture…”

mentioning

confidence: 99%

Precision landing using an adaptive fuzzy multi-sensor data fusion architecture

Al-Sharman

Emran

Jaradat

et al. 2018

Applied Soft Computing

View full text Add to dashboard Cite

The positional inaccuracies associated with the GPS/INS measurements make the terminal phase of the normal GPS/INS landing system imprecise. To solve this problem, an adaptive fuzzy data fusion algorithm is developed to obtain more accurate state estimates while the vehicle approaches the landing surface. This algorithm takes the translational displacements in x and y from the mounted Optical Flow (OF) sensor and fuses them with the INS attitude measurements and the altimeter measurements. This low cost adaptive algorithm can be used for vertical landings in areas where GPS outages might happen or in GPS denied areas. The adaptation is governed by imposing appropriate assumptions under which the filter measurement noise matrix R is predicted. The R matrix is continuously adjusted through a fuzzy inference system (FIS) based on the Kalman innovative sequence and the applied covariance-matching technique. This adaptive fuzzy Kalman fusion algorithm (AFKF) is used to estimate the vehicle's states while landing is being commanded. AFKF results are compared with these obtained using a classical Kalman estimation technique. The AFKF algorithm shows better states estimates than its conventional counterpart does. Compared to prior landing systems, the proposed low cost AFKF has achieved a precision landing with less than 10 cm maximum estimated position error. Real precision landing flights were conducted to demonstrate the validity of the proposed intelligent estimation method.

show abstract

State Estimation for a Small Scale Flybar-less Helicopter

Cited by 5 publications

References 10 publications

System identification of flybar-less rotorcraft UAV

System identification of flybar-less rotorcraft UAV

A Novel Comprehensive Kinematic and Inverse Dynamic Model for the Flybar-Less Swashplate Mechanism: Application on a Small-Scale Unmanned Helicopter

Precision landing using an adaptive fuzzy multi-sensor data fusion architecture

Contact Info

Product

Resources

About