... operational regimes in: (i) an experimental Rijke-tube apparatus and (ii) an experimental combustion system apparatus. Results of the proposed TL have been validated by comparison with those of two shallow neural networks (NNs)-based TL and another NN having an additional long short-term memory (LSTM) layer...

... Chen. 26 03 2013 06 11 2014 09 01 2015 In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for recurrent neural networks (RNNs) with parametric uncertainties and time-varying delay are studied...

.... Editor: Warren E. Dixon. 15 10 2011 18 08 2012 This paper presents an adaptive output-feedback control method based on neural networks for flexible link manipulator which is a nonlinear nonminimum phase system. The proposed controller comprises a linear, a neuro-adaptive...

... fluid power control systems neural networks Pneumatic systems are very well suited to perform high speed, medium force tasks that only require two stop positions. They present a high power to weight ratio, require low maintenance and use a clean form of energy. However, when the task requires...

...A. Karami-Mollaee; N. Pariz; H. M. Shanechi In this paper, position control of servomotors is addressed. A radial basis function neural network is employed to identify the unknown nonlinear function of the plant model, and then a robust adaptive law is developed to train the parameters...

...Jaho Seo; Amir Khajepour; Jan P. Huissoon The objective of this research is to identify a dynamic model that describes the temperature distribution in a die with uncertain dynamics using a neural network (NN) approach. By using data sets obtained from a finite element analysis (FEA) of the thermal...

... for which the passivity property does not hold, the manipulator is also underactuated, that is, the control input does not drive the link directly, but through the flexible dynamics. Our work offers another possible solution to this open problem. We use three-layer neural networks to represent the system...

...G. Colin; Y. Chamaillard; G. Bloch; A. Charlet This paper describes a real-time control method for non-linear systems based on model predictive control. The model used for the prediction is a neural network because of its ability to represent non-linear systems, its ability to be differentiated...