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An adaptive neural control method is proposed in this paper for the flexible air-breathing hypersonic vehicle (AHV) with constraints on actuators. This scheme firstly converts the original control problem with input constraints into a new control problem without input constraints based on the control input saturation function. Secondly, on the basis of the implicit function theorem, the radial basis function neural network (RBFNN) is introduced to approximate the uncertain items of the model. And the minimal-learning-parameter (MLP) technique is adopted to design the adaptive law for the norm of network weight vector, which significantly reduces calculations. Meanwhile, the finite-time convergence differentiator (FD) is introduced, through which the model state variables and their derivatives are accurately estimated to ensure the control effect. Finally, it is theoretically proved that the closed-loop control system is stable. And the effectiveness of the designed controller is verified by simulation.

Manipulating mutual coupling between antenna array elements is always a critical essential in designing phased arrays. In this article, an X‐shaped strip is applied to decouple a five‐element E‐plane microstrip antenna array, whose adjacent elements' center‐to‐center spacing is only 0.45 λ0. Simulation and measurement results reveal that the proposed array employing the loaded structure exhibits excellent decoupling capability, as in comparison to the reference array, impedance of every port is well matched, mutual coupling between both adjacent elements and nonadjacent ones is efficiently reduced and radiation patterns of every individual patch are markedly corrected. Besides, when beam scanning is performed, the proposed array is equipped with higher gain and lower SLL. The X‐shaped strip predicts a promising application in phased array and a large‐scale array.