Aiming to address the issue of low accuracy in model predictions obtained from fitting frequency domain response curves for small unmanned helicopters during the process of modeling their flight dynamics, this study p...
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Aiming to address the issue of low accuracy in model predictions obtained from fitting frequency domain response curves for small unmanned helicopters during the process of modeling their flight dynamics, this study proposes a system identification algorithm based on the combination of weighted least squares and improved grey wolf optimisation algorithm. The algorithm utilises the weighted least squares method to obtain the initial model structure, optimises the initial model parameters using the improved grey wolf optimisation algorithm, and enhances the local search and global optimisation ability of the greywolfoptimisationalgorithm by introducing an improvedgreywolf subgrouping rule, nonlinear convergence factor and dynamic cooperative rule. Ultimately, this approach establishes a dynamic model for small, unmanned helicopters. The identified model is validated using flight test data, with findings demonstrating that this method achieves higher accuracy in model identification and better fits to frequency domain response curves, thus providing a more accurate reflection of the flight dynamics of small unmanned helicopters.
The greywolfoptimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nat...
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The greywolfoptimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta-heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out;and results show its better performance than the standard GWO algorithm and other well-known meta-heuristic algorithms
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