Adaptive neural network control of quadrotor system under the presence of actuator constraints

Emran, Bara J.; Najjaran, Homayoun

doi:10.1109/smc.2017.8123020

Cited by 9 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include feedback linearization methods [7][8][9] for transforming nonlinear dynamics into equivalent linear forms, backstepping controllers [10], sliding mode controllers [11] for robust stabilization and parameter adaptive laws [12]. Additionally, intelligent control approaches like fuzzy logic [13] and artificial neural networks [14] have been proposed to alleviate the need for precise knowledge of the dynamic model, though they often require substantial computational resources and trial-and-error parameter tuning, which renders these approaches impractical for online implementation. Finally, to counteract external time-varying disturbances affecting quadrotors, disturbance observer-based tracking controllers have been proposed (e.g., [15]).…”

Section: Related Literaturementioning

confidence: 99%

A Reference Governor with Adaptive Performance for Quadrotors under Safety Constraints

Trakas,

Tantoulas,

Bechlioulis

2023

Machines

View full text Add to dashboard Cite

This paper presents a novel robust reference governor (RG) for trajectory tracking of quadrotors. The proposed scheme is characterized by low computational complexity and straightforward gain selection. Moreover, it considers safety constraints regarding speed limits and ensures the stability and the proper operation of the closed-loop system. The proposed scheme imposes user-specified performance attributes on the evolution of the tracking error when the safety constraints allow it. When these constraints are at risk of violation, the proposed RG provides a relaxation of the predefined performance specifications to ensure the stability of the plant. Lyapunov analysis proves the boundedness of the closed-loop signals, while its efficacy is further clarified and verified via extensive comparative experimental results against a well-established PI regulator.

show abstract

Section: Related Literaturementioning

confidence: 99%

A Reference Governor with Adaptive Performance for Quadrotors under Safety Constraints

Trakas,

Tantoulas,

Bechlioulis

2023

Machines

View full text Add to dashboard Cite

show abstract

“…Several recent papers related to UAV dealt with adaptive control-based onneural networks (NNs). In Emran and Najjaran (2017) an adaptive NN control of the quadrotor system with actuator constraints, however the authors did not consider the nonlinear function g i to be fully known. RBF NNs have only one single hidden layer which can be easily tuned.…”

Section: Dynamic Model Of the Quadrotor Uavmentioning

confidence: 99%

Adaptive neural network-based robust <i>H</i><SUB align="right">∞ tracking control of a quadrotor UAV under wind disturbances

Bellahcene

Bouhamida

Denaï

et al. 2021

IJAAC

View full text Add to dashboard Cite

The paper deals with the stabilisation and trajectory tracking control of an autonomous quadrotor helicopter system in the presence of wind disturbances. The proposed adaptive tracking controller uses radial basis function neural networks (RBF NNs) to approximate unknown nonlinear functions in the system. Two controllers are proposed in this paper to handle the modelling errors and external disturbances: H∞ adaptive neural controller H∞-ANC and H∞-based adaptive neural sliding mode controller H∞-ANSMC. The design approach combines the robustness of sliding mode control (SMC) with the ability of H∞ to deal with parameter uncertainties and bounded disturbances. Furthermore, the RBF models are derived using Lyapunov stability analysis. The simulation results show that H∞-ANSMC is able to eliminate the chattering phenomenon, reject perturbation mismatch and leads to a better performance than H∞-ANC. A comparative simulation study between the proposed controllers is presented and the results are discussed.

show abstract

“…Moreover, an adaptive backstepping fault tolerant control is proposed, where the auxiliary system is combined with a command filter to deal with the input saturation so as to limit the magnitude of the actuator fault [25]. In [26], an adaptive nonlinear control algorithm is designed to overcome the input constraint and model uncertainties for small‐sized quadrotors with satisfied control stability. In [27], a body‐rate controller is presented to cope with the motor saturations by prioritizing the control inputs for stabilization and trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

Zhou

Tian

Lin

2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

This paper proposes an adaptive control method for an underactuated complex nonlinear system, that is, unmanned aerial vehicle (UAV), to realize high‐precision trajectory tracking under input saturation constraint and other disturbances via artificial neural network and anti‐saturation auxiliary system. First, the rigid body motion theory is applied to establish a simplified nonlinear dynamic model of the UAV. Then a Lyapunov function is designed with the error surface to form the preliminary control law based on dynamic surface control. Afterwards, a radical basis function neural network is developed and used to estimate and compensate the disturbance, while the input saturation is also addressed via the designed anti‐saturation auxiliary system. Thus the stability and signal consistency of the closed‐loop UAV control system can be proved via the Lyapunov stability theory with appropriately tuned parameters. Simulation experiments are performed to verify that the proposed control algorithm can be used for UAV high‐precision trajectory tracking under time‐varying disturbances.

show abstract

Adaptive neural network control of quadrotor system under the presence of actuator constraints

Cited by 9 publications

References 13 publications

A Reference Governor with Adaptive Performance for Quadrotors under Safety Constraints

A Reference Governor with Adaptive Performance for Quadrotors under Safety Constraints

Adaptive neural network-based robust <i>H</i><SUB align="right">∞ tracking control of a quadrotor UAV under wind disturbances

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

Contact Info

Product

Resources

About