Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

Jiang, Tieying; Li, Jie; Huang, Ke-Wei

doi:10.1016/j.cja.2015.04.005

Cited by 33 publications

(15 citation statements)

References 13 publications

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“…The model outputs are: accelerations Because of noisy nature of test sensore, in order to simulating real sensor outputs, a rational noise is also added to simulated data. Therefore, here, a uniformly distributed random noise is added to the original signal (sig m ), obtained from simulation (Tieying, Jie, & Kewei, 2015);…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Taboo continuous ant colony system method (Nobahari & Sharifi, 2014), is used in Rezaei (2015) to estimate aerodynamic coefficients of a rolling airframe. Propeller z-force and pitching moment coefficients of a small unmanned aerial vehicle are estimated through a modified PSO in Tieying, Jie, and Kewei (2015), which in, accelerations, deflection angle, and pitch rate are taken as observations. A new optimization algorithm called adaptive chaotic mutation PSO is proposed to perform APE for a spinning symmetrical projectile (Guan et al, 2016), where, only aerodynamic drag and lift coefficients are estimated.…”

Section: Introductionmentioning

confidence: 99%

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New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

Mohamad¹,

Karimi²,

Naderi³

2020

Aviation

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In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

Mohamad¹,

Karimi²,

Naderi³

2020

Aviation

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“…Under the typical condition, the modified particle swarm optimization (MPSO) algorithm is utilized to determine the optimal parameters [24]. Considering the parameters matching of crashworthiness and lightweight performance of B-pillar as a multi-objective optimization design problem, the updating functions of particles' optimization speed and positions can be expressed as:…”

Section: Mpsomentioning

confidence: 99%

Research on crashworthiness and lightweight of B-pillar based on MPSO with TOPSIS method

Wang

2019

J Braz. Soc. Mech. Sci. Eng.

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To better improve the lightweight and crashworthiness performance of B-pillar, this paper presents a hybrid approach combining the modified particle swarm optimization algorithm (MPSO) and the technique for order preference by similarity to ideal solution (TOPSIS) approach with principal component analysis method (PCA) has been applied to multi-objective lightweight optimization design of the outer panel of the B-pillar structure of automotive. First, the numerical model was conducted to investigate the crashworthiness indicators of H-point under the side collision situation, which agree well with experimental results. Second, a steel-aluminum B-pillar was constructed and through integrating experimental design method with a surrogate model based on the numerical model to construct the approximate model, MPSO algorithm was adopted for multi-objective optimization design of B-pillar and the Pareto solutions set was determined. Finally, TOPSIS with PCA method can obtain the best compromise solution. It is observed that the weight of the B-pillar is reduced by 20.7% under the precondition of the peak of impacting velocity and the peak intrusion deformation has improved by 23.5% and 41.7%, respectively. Moreover, a comparison of the optimal design and original model, it reveals that the proposed hybrid method has a superior performance used for multi-objective lightweight optimization design of B-pillar structure. Keywords B-pillar • Steel-aluminum • Modified particle swarm optimization algorithm (MPSO) • Technique for order preference by similarity to ideal solution (TOPSIS) • Principal component analysis (PCA)

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“…Consequently, an analysis guided by flight data appears to be the best option [4]. Aerodynamic parameter identification is the most developed field in conventional aircraft system identification and has been successfully applied to aircraft and missiles [5]. Suk et al [6] used maximum likelihood estimation and the extended Kalman filter to identify the system of a UAV in 2003.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameter Estimation of a Symmetric Projectile Using Adaptive Chaotic Mutation Particle Swarm Optimization

Guan

Chang

et al. 2016

Mathematical Problems in Engineering

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This article details a new optimizing algorithm called Adaptive Chaotic Mutation Particle Swarm Optimization (ACM-PSO). The new algorithm is used to perform aerodynamic parameter estimation on a spinning symmetric projectile. The main creative ideas of this new algorithm are as follows. First, a self-adaptive weight function is used so that the inertial weight can be adjusted dynamically by itself. Second, the initialized particle is generated by chaos theory. Last, a method that can be used to judge whether the algorithm has fallen into a local optimum is established. The common testing function is used to test the new algorithm, and the result shows that, compared with the basic particle swarm optimization (PSO) algorithm, it is more likely to have a quick convergence and high accuracy and precision, leading to extensive application. Simulated ballistic data are used as testing data, and the data are subjected to the new algorithm to identify the aerodynamic parameters of a spinning symmetric projectile. The result shows that the algorithm proposed in this paper can effectively identify the aerodynamic parameters with high precision and a quick convergence velocity and is therefore suitable for use in actual engineering.

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Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

Cited by 33 publications

References 13 publications

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

Research on crashworthiness and lightweight of B-pillar based on MPSO with TOPSIS method

Aerodynamic Parameter Estimation of a Symmetric Projectile Using Adaptive Chaotic Mutation Particle Swarm Optimization

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