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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)
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)
Magnetostrictive actuators featuring high energy densities, large strokes, and fast responses are playing an increasingly important role in micro/nano-positioning applications. However, such actuators with different input frequencies and mechanical loads exhibit complex dynamics and hysteretic behaviors, posing a great challenge on applications of the(More)