Backstepping approach for design of PID controller with guaranteed performance for micro-air UAV

Kartal, Yusuf; Kolaric, Patrik; Lopez, Victor G.; Dogan, Atilla; Lewis, Frank L.

doi:10.1007/s11768-020-9145-y

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…In this scenario, telemetry data obtained from custom flights are used to model real-time tracking obtained from RF sensors 33 , e.g., the DJI’s Aerospace RF sensor which can detect and track all DJI RF Drones (70%—estimated market share in the drone industry). Here, the measurements are assumed to belong from a continuous-time model 34 of the form where is the state vector composed of the linear positions and velocities in the Cartesian space, m is the mass of the drone, g is the gravity acceleration, μ is the total thrust, and ϕ , θ , and ψ denote the roll, pitch and yaw Euler angles 35 . In this scenario, it is assumed that measurements of the state x and the inputs u = [ ϕ , θ , ψ , μ ] ⊤ are available with some Gaussian distributed noise 36 .…”

Section: Methodsmentioning

confidence: 99%

Uncovering drone intentions using control physics informed machine learning

Perrusquía,

Guo,

Fraser

et al. 2024

Commun Eng

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Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.

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Section: Methodsmentioning

confidence: 99%

Uncovering drone intentions using control physics informed machine learning

Perrusquía,

Guo,

Fraser

et al. 2024

Commun Eng

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show abstract

“…In this scenario, we use telemetry data obtained from custom flights to model real-time tracking obtained from RF sensors [39], e.g., the DJI's Aerospace RF sensor which can detect and track all DJI RF Drones (70% -estimated market share in the drone industry). Here, the measurements are assumed to belong from a continuous-time model [40,41,42] of the form…”

Section: Rf Sensormentioning

confidence: 99%

Uncovering Drone Intentions using Control Physics Informed Machine Learning

Perrusquía

Guo

Fraser

et al. 2023

Preprint

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Fully autonomous aerial platforms or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity nor file flight plans and can pose a potential risk to a variety of critical infrastructures. Understanding an uncooperative drone's intention is important to assigning risk and executing countermeasures. Drones have rapidly changing design, flexible capabilities, and diverse underpinning algorithms. This makes distinguishing malicious from naive intentions across platforms difficult. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes (e.g., via inverse learning) using observational data alone is inherently unreliable due to observational and learning bias. Here we develop a novel control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes, out performing over expert-defined anomaly detection and inverse learning approaches. Our CPhy-ML complementary learning couples the representation power of deep learning with the conservation laws of aerospace control models, reducing bias and instability. The results in simulation and experimentation demonstrate that we can find common intention patterns across the inferred intention classes, ranging from trajectory to reward goals. We believe that this framework can provide deeper insight into the complex nature of intention and a firm step towards to its smooth integration in current counter drone technologies. The techniques developed here can aid the enforcement of drone incursions in a time where geo-fencing is no-longer efficiently enforceable. As transport and operational autonomy is increasingly used, enforcement algorithms can also inform the design of better criminal justice system and safer autonomous systems themselves.

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“…Many nonlinear and linear controls have therefore been established given these circumstances. For instance, adaptive control [4][5][6], backstepping control [7,8], sliding mode control [9][10][11][12][13][14], optimal control [15,16] and intelligent control [17][18][19][20][21] have been deployed.…”

Section: Introductionmentioning

confidence: 99%

Position and Attitude Tracking of MAV Quadrotor Using SMC-Based Adaptive PID Controller

Noordin

Basri

Mohamed

2022

Drones

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A micro air vehicle (MAV) is physically lightweight, such that even a slight perturbation could affect its attitude and position tracking. To attain better autonomous flight system performance, MAVs require good control strategies to maintain their attitude stability during translational movement. However, the available control methods nowadays have fixed gain, which is associated with the chattering phenomenon and is not robust enough. To overcome the aforementioned issues, an adaptive proportional integral derivative (PID) control scheme is proposed. An adaptive mechanism based on a second-order sliding mode control is used to tune the parameter gains of the PID controller, and chattering phenomena are reduced by a fuzzy compensator. The Lyapunov stability theorem and gradient descent approach were the basis for the automated tuning. Comparisons between the proposed scheme against SMC-STA and SMC-TanH were also made. MATLAB Simulink simulation results showed the overall favourable performance of the proposed scheme. Finally, the proposed scheme was tested on a model-based platform to prove its effectiveness in a complex real-time embedded system. Orbit and waypoint followers in the platform simulation showed satisfactory performance for the MAV in completing its trajectory with the environment and sensor models as perturbation. Both tests demonstrate the advantages of the proposed scheme, which produces better transient performance and fast convergence towards stability.

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Backstepping approach for design of PID controller with guaranteed performance for micro-air UAV

Cited by 15 publications

References 12 publications

Uncovering drone intentions using control physics informed machine learning

Uncovering drone intentions using control physics informed machine learning

Uncovering Drone Intentions using Control Physics Informed Machine Learning

Position and Attitude Tracking of MAV Quadrotor Using SMC-Based Adaptive PID Controller

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