Nonlinear inversion method based on dynamics model modification

Faping Zhang,Kai Wu,L.H Ky,A.Y Bani Hashim,C.W Tong,C.H Su,M Islam,Y Xia

doi:10.1051/matecconf/201928801003

Faping Zhang, Kai Wu + Show 6 more

Open Access

https://doi.org/10.1051/matecconf/201928801003

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Abstract

In the fields of modern aviation system, subgrade vehicle system and complex mechanical system, there is a problem that parameters of most dynamic models are inaccurate. This problem results in a large difference between the model results and the experimental results. In order to solve this problem, this paper build a nonlinear inversion method based on dynamics model modification (NIDM). Firstly, the error relationship was obtained by integrating the experimental data with the simulation results of the forward modelling model by the cost function and penalty function. Then, the problem of error function minimization was solved by using the parameter iteration generated by particle swarm optimization algorithm, and the corrected parameters of the forward modelling model were obtained. Finally, the method was tested by building a vehicle suspension vibration model and a pavement excitation model as test samples. The test results show that the fitting degree between the simulation results and the experimental results can be effectively improved by modifying the parameters of the dynamic model based on the NIDM method.

Highlights

In the design and development of roadbed mobile platforms, aerospace vehicles, and complex mechanical equipment, in order to reduce the use of physical prototypes so that to reduce the test cost and shorten the development cycle, it is necessary to construct a dynamic model to simulate the products under development
Dynamic models tend to be less accurate at the beginning of modeling and the model parameters need to be modified
In order to improve the accuracy of the model simulation results, the parameters of the dynamic model need to be modified according to the limited number of physical prototype test results

Summary

Introduction

In the design and development of roadbed mobile platforms, aerospace vehicles, and complex mechanical equipment, in order to reduce the use of physical prototypes so that to reduce the test cost and shorten the development cycle, it is necessary to construct a dynamic model to simulate the products under development. In order to improve the accuracy of the model simulation results, the parameters of the dynamic model need to be modified according to the limited number of physical prototype test results. Writers in [3] used simulated annealing algorithm to optimize the multi-objective global optimization of rocket nozzle model and improve the accuracy and efficiency of correction. The test data is combined with the simulation results of the forward model to obtain the error relation function. The value range of the optimized variable is taken as a constraint condition to construct the objective function and the relevant parameters of the particle swarm optimization algorithm are set. The PSOTCF algorithm is implemented, the output that are the optimal value of the variables which can minimize the error between the simulation result and the test result will be obtained by iterative solution

Particle swarm optimization with timevarying constrict factors algorithm

Test case: parameter modification of chassis suspension vibration model

Part of the vibration model

Part of the road surface input

Construction of constraint conditions

Result and dissuasion