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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 been overlooked(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)
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