Interval Type-3 T-S fuzzy system for nonlinear aerodynamic modeling

Singh, Dhan Jeet; Verma, Nishchal K.

doi:10.1016/j.asoc.2023.111097

Cited by 5 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [71], the authors presented an aircraft-related application of the IT3 TS SFLS fuzzy system for nonlinear aerodynamic modeling from recorded flight data, using trial and error and manual tuning of various constants to adjust antecedent and consequent parameters. In [72,73], the design for the construction of the IT2 TS SFLS uses the type-2 modified interval fuzzy c-regression model clustering algorithm and the hyperplane shape membership function.…”

mentioning

confidence: 99%

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Castorena,

Méndez,

López-Juárez

et al. 2024

Mathematics

View full text Add to dashboard Cite

This paper presents the novel enhanced Wagner–Hagras interval type-3 Takagi–Sugeno–Kang fuzzy logic system with type-1 non-singleton inputs (EWH IT3 TSK NSFLS-1) that uses the backpropagation (BP) algorithm to train the antecedent and consequent parameters. The proposed methodology dynamically changes the parameters of only the alpha-0 level, minimizing some criterion functions as the current information becomes available for each alpha-k level. The novel fuzzy system was applied in two industrial processes and several fuzzy models were used to make comparisons. The experiments demonstrated that the proposed fuzzy system has a superior ability to predict the critical variables of the tested processes with lower prediction errors than those produced by the benchmark fuzzy systems.

show abstract

mentioning

confidence: 99%

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Castorena,

Méndez,

López-Juárez

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

Type-3 Fuzzy Logic in Time Series Prediction

Castillo,

Melin

2024

SpringerBriefs in Applied Sciences and Technology

View full text Add to dashboard Cite

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

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

Interval Type-3 T-S fuzzy system for nonlinear aerodynamic modeling

Cited by 5 publications

References 32 publications

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Type-3 Fuzzy Logic in Time Series Prediction

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

Contact Info

Product

Resources

About