IT2-Neuro-Fuzzy Wavelet Network with Jordan Feedback Structure for the Control of Aerial Robotic Vehicles with External Disturbances

Kumar, Rahul; Singh, Uday Pratap; Bali, Arun; Chouhan, Siddharth Singh

doi:10.1007/978-3-031-29447-1_17

Studies in Computational Intelligence

2023

DOI: 10.1007/978-3-031-29447-1_17

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IT2-Neuro-Fuzzy Wavelet Network with Jordan Feedback Structure for the Control of Aerial Robotic Vehicles with External Disturbances

Rahul Kumar

Uday Pratap Singh

Arun Bali

et al.

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Cited by 3 publications

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Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Cai,

Zhang,

Khadakar

et al. 2024

Complex Intell. Syst.

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Unmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

show abstract

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Cai,

Zhang,

Khadakar

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

Adaptive control of unknown fuzzy disturbance-based uncertain nonlinear systems: application to hypersonic flight dynamics

Kumar,

Singh,

Bali

et al. 2023

J Anal

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Structural Approach to Convolutional Neural Network Trained With Novel Scaled Matrix Image for Pseudo Real-Time Power Quality Event Monitoring

Mishra,

Mallick,

Gadanayak

et al. 2024

IEEE Access

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IT2-Neuro-Fuzzy Wavelet Network with Jordan Feedback Structure for the Control of Aerial Robotic Vehicles with External Disturbances

Cited by 3 publications

References 25 publications

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Adaptive control of unknown fuzzy disturbance-based uncertain nonlinear systems: application to hypersonic flight dynamics

Structural Approach to Convolutional Neural Network Trained With Novel Scaled Matrix Image for Pseudo Real-Time Power Quality Event Monitoring

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