Xiucai Huang

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This paper considers the tracking control problem for a class of multi-input multi-output nonlinear systems subject to unknown actuation characteristics and external disturbances. Neuroadaptive proportional–integral (PI) control with self-tuning gains is proposed, which is structurally simple and computationally inexpensive. Different from(More)
This paper shows that the structurally simple and computationally inexpensive PID control, popular with SISO linear time-invariant systems, can be generalized and extended to control nonlinear MIMO systems with nonparametric uncertainties and actuation failures. By utilizing the Nussbaum-type function and the matrix decomposition technique, non-square(More)
The “universal” approximating/learning feature of neural network (NN), widely and extensively used for control design, is contingent upon some critical conditions, either of which, if not satisfied, would render such feature vanished. In this paper, we show that these conditions are literally linked with several fundamental issues that have(More)
In this paper, we present a neuroadaptive control for a class of uncertain nonlinear strict-feedback systems with full-state constraints and unknown actuation characteristics where the break points of the dead-zone model are considered as time-variant. In order to deal with the modeling uncertainties and the impact of the nonsmooth actuation(More)
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