Evaluation of Reduction in the Performance of a Small UAV After an Aileron Failure for an Adaptive Guidance System

Ducard, Guillaume; Kulling, K.C.; Geering, Hans P.

doi:10.1109/acc.2007.4282845

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…Based on the geometric and kinematics properties, cylindrical volumn models for NFZ satisfy lateral guidance control law of UAVs which are commonly adopted for the design of flight path, e.g. [27], [28]. When a UAV has to avoid flying over NFZs, a centripetal acceleration on the UAV is generated to help the UAV change its aviation direction.…”

Section: A Signal Modelmentioning

confidence: 99%

Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper

Wei

Yang

et al. 2020

IEEE Trans. Commun.

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In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes. R. Li and J. An are with the secure UAV communication systems with a single-UAV was studied in [8], [14]-[16] with different system settings. However, due to the stringent requirements on UAV's size, weight, and power (SWAP), the performance achieved by deploying a single-UAV is still limited [6]. To achieve a higher efficiency in secure communications, multi-UAV cooperation was adopted in [17]-[20]. In particular, a jammer UAV can fly close to a potential eavesdropper based on demand by leveraging its mobility and opportunistically transmits artificial noise signal deliberately to combat the eavesdropping channels [17]. To improve the system security performance, [18] and [19] presented a cooperative jamming approach to safeguard the UAV's communication by exploiting artificial jamming transmission from other friend UAVs in the existence of a single-eavesdropper. With the consideration of fairness in two-UAV secure communications, [20] investigated the joint power allocation and trajectory design for the maximization of the minimum secrecy rate per user when one UAV is dispatched to convey confidential messages to a ground user where another cooperative UAV transmits a jamming signal. However, in [17]-[20], the role of the UAV is fixed where a communication/jamming UAV can only provide either communication/jamming signal during the whole time horizon.In contrast, a multi-purpose UAV, which can dynamically serve as a communication UAV or a jamming UAV, provides a high flexibility in trajectory design for secure UAV communications. March 17, 2020 DRAFT 3 For instance, when a communication UAV flies closer to an eave...

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Section: A Signal Modelmentioning

confidence: 99%

Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper

Wei

Yang

et al. 2020

IEEE Trans. Commun.

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“…It is possible to find works, such as [17], which studies the influence in failures of the drones physical components and assess a change of performance after an occurrence of some damage.…”

Section: Related Workmentioning

confidence: 99%

“…The work of [17], as well in this research, analyzes the drone behavior during the flight. Nevertheless, the authors focus on analyzing vehicle behavior after the occurrence of failures in the aileron.…”

Section: Related Workmentioning

confidence: 99%

An Environment for Indoor Testing and Diagnosis of Drones using Co-simulation

Abreu¹,

Oliveira

Silva

et al. 2019

Anais Estendidos Do IX Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2019)

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Operations with Unmanned Aerial Vehicles (UAVs) require reliability to execute missions. With the correct diagnostic, it is possible to predict vehicle failure during or before the flight. The objective of this work is to present a testing tool, which analyzes and evaluates drones during the flight in indoor environments. For this purpose, the framework Ptolemy II was extended for communication with real drones using the High-Level Architecture (HLA) for data exchanging and synchronization. The presented testing environment is extendable for other testing routines and is ready for integration with other simulation and analysis tools. In this paper, two failure detection experiments were performed, with a total of 20 flights for each one, which 80\% were used to train a decision tree algorithm, and the other 20% flights to test the algorithm in which one of the propellers had an anomaly. The failure rate or detection rate was 70\% for the first experiment and 90% for the second one.

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“…In this work, the disturbance model is simply a constant value. The objective of the control system is to asymptotically eliminate the tracking error given a reference command r ss , that is, (2) in the presence of a disturbance d k and given constraints on the state and control vectors x k 2 X, u k 2 U, where X and U are closed, bounded and convex sets expressed by linear inequalities. This problem corresponds to finding a new equilibrium point for the plant in steady state, which is essentially a determination of the steadystate target vectors x ss r ss ; d k and u ss r ss ; d k and is expressed by…”

Section: Set Of Statically Admissible Commandsmentioning

confidence: 99%

“…Strube et al [1] proposed the creation of a database for different fault conditions with related achievable trim conditions, which could be employed by outer control loops. Ducard et al [2] has evaluated the reduced performance of an unmanned aerial vehicle after an aileron failure in terms of admissible bank angles and time to roll to the limiting angle.…”

Section: Introductionmentioning

confidence: 99%

Reference Management for Fault-Tolerant Model Predictive Control

Almeida

2011

Journal of Guidance, Control, and Dynamics

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This work presents a reference management technique for fault-tolerant model predictive control. The reference value to be tracked is first filtered through a set of statically admissible commands. This set expresses the airframe's physical ability to achieve a new equilibrium point, and it is constructed by considering the limitations of the inputs and states of the system under control. The set has a polyhedral form defined by linear inequalities. State and control vectors are obtained at steady state to guarantee the feasibility of the constrained numerical problem faced by the model predictive control regulator. The proposed method has been evaluated using a linear model of a fighter aircraft, and it demonstrated adequate performance with a computational burden compatible with real-time applications.augmented observation matrix e = model following error H = tracking matrix I = identity matrix J = cost function K d = feedback gain matrix k ss , k N = linear inequalities vectors L, L d , L x = observer gain matrices L d , H = auxiliary observer matrices L ss , M x , M x ss , M u ss , M d = linear inequalities matrices N = control horizon O 1 = invariant set O 1 = tracking invariant set P = terminal weighting matrix P m = set of statically admissible commands given the current disturbance P ss = steady-state polyhedron P w = set of statically admissible commands p, q, r = aircraft angular rates, rad=s Q, W, R = weighting matrices Q d , W d , R d = discrete weighting matrices Q m , W m , R m = implicit model following weighting matrices Q ss , R ss = constrained target calculator weighting matrices r c = command (demand) vector r ss = statically admissible command vector T = rotation matrix T s = sampling time, s t = time, s u max , u min = bounds over control vector w = performance output vector w m = reference model state vector X, U = admissible state and control sets X N = set of admissible extended state vectors X ss N = set of admissible steady-state control and state vectors x, u = state and control vectors x a = augmented state vector x max , x min = bounds over state vector x ss , u ss = state and control vectors at steady state x 0 = initial state vector x,d = estimated state and disturbance vectors x,ũ = tracking state and control vectors y = observed vector z = controlled output vector z ss = feasible command vector ,= angles of attack and sideslip, rad c , r = canard and rudder deflections, rad r e , l e = right and left elevon deflections, rad x , z = tolerance vectors = projection operator = slice operator , , d = discrete-time state-space matrices = auxiliary discrete state-space matrix a , a = augmented discrete state-space matrices

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Evaluation of Reduction in the Performance of a Small UAV After an Aileron Failure for an Adaptive Guidance System

Cited by 17 publications

References 6 publications

Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper

Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper

An Environment for Indoor Testing and Diagnosis of Drones using Co-simulation

Reference Management for Fault-Tolerant Model Predictive Control

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