Dynamic aerodynamic parameter estimation using a dynamic particle swarm optimization algorithm for rolling airframes

Mohamad, Ayham; Karimi, Jalal; Naderi, Alireza

doi:10.1007/s40430-020-02658-y

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The position-velocity model is used to iteratively update the particle, and finally the particle is guided to the global optimal solution. Because of its simple structure, easy implementation, good global search ability, and fast convergence speed, it is widely used in dynamic optimization problems [16] . But the traditional PSO is easy to fall into local optimum [17] , Xia [15] introduces a state-based adaptive velocity limit strategy (SAVL), chaos optimization strategy, adaptive update strategy and mutation strategy to improve the performance of the algorithm.…”

Section: Campso-savlmentioning

confidence: 99%

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Xia,

Yuan,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Aerodynamic parameters play a key role in determining the trajectory and stability of projectiles. In order to weaken the impact of the projectile movement model, the PSO variant named chaos adaptive mutation PSO with a stated based adaptive velocity limit (CAMPSO-SAVL) is applied to solve the projectile identification equation (based on maximum likelihood criterion), and output the optimal estimation of aerodynamic parameters. Ballistic reconstruction is carried out based on the identification results. The reconstruction results show that the identification results of CAMPSO-SAVL have excellent accuracy and can meet the engineering needs.

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Section: Campso-savlmentioning

confidence: 99%

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Xia,

Yuan,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…Ji-gang et al [37] combined the advantage of PSO in the initial value section and the advantages of the Newton iteration method in precise iteration and successfully identified the drag coefficient of the projectile. Mohamad et al [38] put forward the concept of dynamic parameter estimation (DAPE).…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Wang

Xia

2023

International Journal of Aerospace Engineering

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The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. To obtain the aerodynamic parameters of the projectile accurately, an aerodynamic parameter identification model based on ensemble learning theory and ELM optimized by improved particle swarm optimization is proposed. The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. Combined with the ensemble learning theory, the improved ELM neural network is regarded as a weak learner to generate a strong learner. The structural parameters of the strong learner were continuously optimized through training, and an aerodynamic parameter identification model of projectile based on ensemble learning theory is obtained. The simulation results show that the introduction of the IPSO and ensemble learning theory enables the model to exhibit excellent generalization ability. The proposed identification model can accurately describe the variation of aerodynamic parameters with the Mach number.

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“…Conventional methods for enhancing low-fidelity computational models with high-fidelity or experimental data encompass techniques like Bayesian Inference [2], [3], Gradient-Based and Gradient-Free Optimization [4], [5], among others. The primary goal of these techniques is to determine the optimal parameters of the low-fidelity model in order to improve its overall performance.…”

Section: Introductionmentioning

confidence: 99%

Physics Infused Machine Learning Based Prediction of VTOL Aerodynamics with Sparse Datasets

Oddiraju

Amin²,

Piedmonte³

et al. 2023

AIAA AVIATION 2023 Forum

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Complex optimal design and control processes often require repeated evaluations of expensive objective functions and consist of large design spaces. Data-driven surrogate models such as neural networks and Gaussian processes provide an attractive alternative to expensive simulations and are utilized frequently to represent these objective functions in optimization. However, pure data-driven models, due to a lack of adherence to basic physics laws and constraints, are often poor at generalizing and extrapolating. This is particularly the case, when training occurs over sparse high-fidelity datasets. A class of Physicsinfused machine learning (PIML) models integrate ML models with low-fidelity partial physics models to improve generalization performance while retaining computational efficiency. This paper presents two potential approaches for Physics infused modelling of aircraft aerodynamics which incorporate Artificial Neural Networks with a low-fidelity Vortex Lattice Method model with blown wing effects (BLOFI) to improve prediction performance while also keeping the computational cost tractable. This paper also develops an end-to-end auto differentiable open-source framework that enables efficient training of such hybrid models. These two PIML modelling approaches are then used to predict the aerodynamic coefficients of a 6 rotor eVTOL aircraft given its control parameters and flight conditions. The models are trained on a sparse high-fidelity dataset generated using a CHARM model. The trained models are then compared against the vanilla low-fidelity model and a standard pure data-driven ANN. Our results show that one of the proposed architecture outperforms all the other models and at a nominal increase in execution time. These results are promising and pave way to PIML frameworks which are able generalize over different aircraft and configurations thereby significantly reducing costs of design and control.

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Dynamic aerodynamic parameter estimation using a dynamic particle swarm optimization algorithm for rolling airframes

Cited by 7 publications

References 15 publications

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Particle swarm optimization algorithm variant: applying to the parameter identification of the projectile

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Physics Infused Machine Learning Based Prediction of VTOL Aerodynamics with Sparse Datasets

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