Fixed-Time Extended Disturbance Observer-Based Robust Control for Quadrotor Vehicle

Ullah, Mati; Zhao, Chunhui; Maqsood, Hamid

doi:10.1109/ccdc52312.2021.9601923

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The idea of SMC is derived from the variable structure control which is mainly applied for the control and stability of multi-input multi-output systems. SMC is a nonlinear control methodology that forces the system’s structure and control law to modify according to the change in the pre-defined sliding manifold in state-space, which guarantees the tracking error tends to zero (Ullah et al , 2021; Maqsood and Qu, 2020; Ali et al , 2020).…”

Section: Improved Radial Basis Function Neural Network (Irbfnn)mentioning

confidence: 99%

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

Ullah

Zhao

Maqsood³

et al. 2021

IJIUS

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PurposeThis paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.Design/methodology/approachThe proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.FindingsThe performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.Practical implicationsFor the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.Originality/valueThe proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

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Section: Improved Radial Basis Function Neural Network (Irbfnn)mentioning

confidence: 99%

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

Ullah

Zhao

Maqsood³

et al. 2021

IJIUS

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“…Sliding-mode control (SMC) is considered one of the influencing control schemes for nonlinear systems affected by exogenous disturbances (Zhihong et al, 1994;Ullah et al, 2021aUllah et al, , 2021bZou, et al, 2011). In Jia et al (2017), Parma et al (2003), Mu et al (2017) and Hassani et al (2019), SMC and back-stepping techniques are proposed to get rid of the problems associated with the linear controllers.…”

Section: Introductionmentioning

confidence: 99%

“…Because of their significant performance, they have now been widely used in the quadrotor's control. They can significantly improve the motion control of the quadrotor by adopting variations in system dynamics and controller parameters according to the conditions (Alqaisi et al, 2019;Moawad et al, 2019;Ullah et al, 2021aUllah et al, , 2021bLuo et al, 2019). For example, for a quadrotor following desired position and attitude in the presence of disturbances and parametric uncertainties, an adaptive SMC based on a neural network (NN) is proposed in Razmi and Afshinfar (2019).…”

Section: Introductionmentioning

confidence: 99%

Improved radial basis function artificial neural network and exact-time extended state observer based non-singular rapid terminal sliding-mode control of quadrotor system

Ullah

Zhao

Maqsood³

2022

AEAT

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Purpose The purpose of this paper is to design a hybrid robust tracking controller based on an improved radial basis function artificial neural network (IRBFANN) and a novel extended-state observer for a quadrotor system with various model and parametric uncertainties and external disturbances to enhance the resiliency of the control system. Design/methodology/approach An IRBFANN is introduced as an adaptive compensator tool for model and parametric uncertainties in the control algorithm of non-singular rapid terminal sliding-mode control (NRTSMC). An exact-time extended state observer (ETESO) augmented with NRTSMC is designed to estimate the unknown exogenous disturbances and ensure fast states convergence while overcoming the singularity issue. The novelty of this work lies in the online updating of weight parameters of the RBFANN algorithm by using a new idea of incorporating an exponential sliding-mode effect, which makes a remarkable effort to make the control protocol adaptive to uncertain model parameters. A comparison of the proposed scheme with other conventional schemes shows its much better performance in the presence of parametric uncertainties and exogenous disturbances. Findings The investigated control strategy presents a robust adaptive law based on IRBFANN with a fast convergence rate and improved estimation accuracy via a novel ETESO. Practical implications To enhance the safety level and ensure stable flight operations by the quadrotor in the presence of high-order complex disturbances and uncertain environments, it is imperative to devise a robust control law. Originality/value A new idea of incorporating an exponential sliding-mode effect instead of conventional approaches in the algorithm of the RBFANN is used, which makes the control law resistant to model and parametric uncertainties. The ETESO provides rapid and accurate disturbance estimation results and updates the control law to overcome the performance degradation caused by the disturbances. Simulation results depict the effectiveness of the proposed control strategy.

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Adaptive Sliding Mode Control with an Extended Disturbance Observer for a Quadcopter Unmanned Aerial Vehicle: A Finite-Time Convergent Approach

Khan,

Gao,

et al. 2023

2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)

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Fixed-Time Extended Disturbance Observer-Based Robust Control for Quadrotor Vehicle

Cited by 4 publications

References 21 publications

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

Improved radial basis function artificial neural network and exact-time extended state observer based non-singular rapid terminal sliding-mode control of quadrotor system

Adaptive Sliding Mode Control with an Extended Disturbance Observer for a Quadcopter Unmanned Aerial Vehicle: A Finite-Time Convergent Approach

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