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In this paper, an adaptive neural network (NN) dynamic surface control is proposed for a class of time-delay nonlinear systems with dynamic uncertainties and unknown hysteresis. The main advantages of the developed scheme are: 1) NNs are utilized to approximately describe nonlinearities and unknown dynamics of the nonlinear time-delay systems, making it(More)
This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and(More)
In this paper, a new neural-network-based hysteresis model is presented. First of all, a variable-power hysteretic operator is proposed via the characteristics of the motion point trajectory of hysteresis for magnetostrictive actuators. Based on the variable-power hysteretic operator, a basic hysteresis model is obtained. And then, a two-dimension input(More)
In this paper, an novel neural approximator based decentralized output feedback adaptive dynamic surface inverse control (DSIC) scheme is proposed for a class of larger-scale time delay systems preceded by unknown asymmetric hysteresis. The main features are as follows: 1) to our best knowledge, it is for the first time to use the neural networks and(More)