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NN model-based evolved control by DGM model for practical nonlinear systems
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AI
DOI：10.1016/j.eswa.2021.115873.png)
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摘要
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This article develops an evolutionary bat algorithm (EBA) based fuzzy neural network (NN), which is applied to the adaptive controller in a plant, for the purpose of ensuring the asymptotic stability and heightening the stability of the vehicle. With a gray signal predictor, the Lyapunov theory as well as the backstepping method are adopted to ensure the non-linearity of the controlled system and derive the evolutionary control law of the signal tracking. The discrete gray model (DGM) (2,1) is used to predict the future motion of the nonlinear system, so that the fuzzy controller can ensure the Lyapunov stability and the feasibility of the parallel distributed compensation (PDC) scheme through the Lyapunov type lemma. The controller design is demonstrated in practical nonlinear systems, for a mechanical elastic wheel (MEW), as a feasible mathematical framework for the matching of this circular object revolving on an axle.
Keyword:
Lyapunov energy function
Evolved control
Adaptive backstepping
Gray predictor
Neural network
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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