Real Time Parameter Identification of the Inertia Tensor for a Quad-rotor mini-aircraft using Adaptive Control

López, R. J.; Machón-González, Iván; Flores, Joel; Ordaz, Jairo; Salazar, Sergio; Lozano, Rogelio

doi:10.3182/20131120-3-fr-4045.00040

Cited by 4 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(42) compensates the variation of these conditions based on the specified bounds in Eqs. (32) and (34). Hence any variation in these two disturbances under specified bounds results in stable control of the aircraft.…”

Section: Environmental Conditionsmentioning

confidence: 98%

“…However, to further increase the robustness of our control scheme for more reliable performance, a test can be executed before the flight to make an estimation of the range of uncertainty in terms of the payload changes, i.e. more accurate estimation of λ min , λ max , and δ. known methods such as in [34,35,36] can be used to estimate the inertia matrix while in flight and disallow the flight if the λ min , λ max , δ are violated.…”

Section: Nominal Performancementioning

confidence: 99%

See 1 more Smart Citation

A robust controller for multi rotor UAVs

Jasim

Veres

2020

Aerospace Science and Technology

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are safety-critical systems that often need to fly near buildings and over people under adverse wind conditions and hence require high manoeuvrability, accuracy, fast response abilities to ensure safety. Under extreme conditions, the dynamics of these systems are strongly nonlinear and are exposed to disturbances, which need a robust controller to keep the UAV and its environment safe. In this paper a novel robust nonlinear multi-rotor controller is introduced based on essential modifications of standard dynamic inversion control, which makes it insensitive to payload changes and also to large wind gusts. First a robust attitude controller is

show abstract

Section: Environmental Conditionsmentioning

confidence: 98%

Section: Nominal Performancementioning

confidence: 99%

A robust controller for multi rotor UAVs

Jasim

Veres

2020

Aerospace Science and Technology

View full text Add to dashboard Cite

show abstract

“…In a specifically aerospace context, PID has been applied to control of quad-rotor aircraft. The parameters of the moments of inertia of the aircraft were identified based on an adaptive control scheme, and showed how this type of identification could be used to improve performance [26]. PID has also been considered in both the time and frequency domains for providing estimates of dimensionless stability derivatives online as a means of detecting damages to aerodynamic control surfaces [27].…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

Artificial Immune System for Unmanned Aerial Vehicle Abnormal Condition Detection and Identification

McLaughlin¹

View full text Add to dashboard Cite

A detection and identification scheme for abnormal conditions was developed for an unmanned aerial vehicle (UAV) based on the artificial immune system (AIS) paradigm. This technique involves establishing a body of data to represent normal conditions referred to as "self" and differentiating these conditions from abnormal conditions, referred to as "non-self". Data collected from simulation of the UAV attempting to autonomously fly a pre-decided trajectory were used to develop and test a scheme that was able to detect and identify aircraft sensor and actuator faults. These faults included aerodynamic control surface locks and damages and angular rate sensor biases. The method used to create the AIS is known as the partition of the universe approach. This approach differs from standard clustering approaches because the universe is divided into uniform partition clusters rather than clustering data using some clustering algorithm. It is simpler and requires less computational resources. This will be the first time that this approach has been applied for use in aerospace engineering. Data collected from nominal flights were used to define self partitions, and the non-self partitions were defined implicitly. The creation scheme is also discussed, involving all software used for simulation, as well as the process of creating the self and the logic behind the detection and identification schemes. The detection scheme was evaluated based on detection rate, detection time, and false alarms for flights under both normal and abnormal conditions. The failure identification scheme was assessed in terms of identification rate and time. Investigation of the proposed technique showed promising results for the cases explored with comparable performance with respect to clustering-based approaches and motivates further research and extension of the proposed methodology toward a more complete health management system.

show abstract