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In this paper, by considering the existence of unavailable state, external disturbance, and denial of service (DoS) attacks, an anti-disturbance trajectory tracking controller is proposed for the air-ground system composed of unmanned autonomous helicopter (UAH) and unmanned ground vehicle (UGV). Initially, by combining the advantages of conventional event-triggered scheme (ETS) and memory ETS, a switching-like event-triggered mechanism is put forward, which can cause less data transmissions without reducing control performance, and effectively restrain the DoS attacks. Secondly, by dividing the DoS attacks into active intervals and sleep ones, the concept of acknowledgement character technology (ACK) is presented to determine the suitable type of time intervals. Thirdly, the switching-like dynamic ETS is introduced for the sleep intervals of DoS attacks while the ETS with fixed triggered-threshold is exploited for the active intervals. Fourthly, based on the occurrence of the DoS attacks, a switching-like observer and a normal one are respectively proposed to estimate the UGV state, the UAH state, the reference input of the UGV, and the disturbance of the UAH, which are utilized to design the anti-disturbance tracking controller. Fifthly, an augmented closed-loop system consisting of observation errors and tracking error is established and its stability is analyzed by using Lyapunov stability theory. Then, a sufficient condition on co-designing the parameters of switching-like ETS, observers, and tracking controller is presented in terms of linear matrix inequalities (LMI). Finally, the validity and superiority of the proposed control scheme are verified by resorting to simulations and comparisons.

Since the neural network was introduced into the super-resolution (SR) field, many SR deep models have been proposed and have achieved excellent results. However, there are two main drawbacks: one is that the methods based on the best PSNR do not have enough comfortable visual quality; the other is that although the SR models based on generative adversarial network (GAN) have satisfactory visual quality, the structure of the reconstructed image has apparent defects. Therefore, according to the characteristics that human eyes are sensitive to high-frequency components in images, this paper proposes an improved image SR GAN model based on high-frequency information fusion (HIFGAN). It builds a feature extraction network for high-frequency information fusion by designing a lightweight spatial attention module and improving the network architecture of ESRGAN. It makes the generator in the GAN network have better feature recovery ability, reduces the dependence of the later training on the decider and loss function, and makes the generated image structure more consistent with the real situation. In addition, we build a high-frequency loss function to optimize the training of the generator network. Detailed experimental results show that HIFGAN performs excellently in both objective criterion evaluation and subjective visual effect. Compared with the state-of-the-art GAN-based SR networks, the reconstructed image by our model is more precise and complete in texture details.