This article proposes an adaptive control for a flexible manipulator (FM) under the influence of distributed disturbances, unknown dead zones, and input quantization. First, the hybrid effect of the unknown dead zone and input quantization is formulated and represented based on some essential transformations. Then, an adaptive robust quantized control with online updating laws is developed to address the uncertainty of the dead zone, ensure robustness and angle position, and dampen the vibration in the FM system. Subsequently, the Lyapunov theoretical analysis is employed to ensure the bounded stability of the system. Finally, numerical simulations and experiments with a Quanser platform are given to further verify the feasibility and superiority of the designed scheme.

This paper proposes an adaptive constrained control considering input saturation and prescribed performance constraints for uncertain two-degree-of-freedom helicopter systems with actuator faults. A devised performance function precisely indicates the time for the system to achieve stability and adjusts the convergence speed of its dynamic process through parameter modifications. The smoothing Gaussian error function is introduced to replace the saturated nonlinearity of each non-smooth actuator, the radial basis function neural network is employed for approximating the unknown nonlinear function, adaptive auxiliary parameters are introduced, and an adaptive controller is designed to address issues such as model uncertainty, input saturation, and actuator faults arising in practical applications. Furthermore, the Lyapunov direct method is employed to demonstrate the stability and boundedness of the helicopter system. Results obtained from simulations and experiments on the Quanser two-degree-of-freedom helicopter experimental platform confirm the effectiveness and feasibility of the proposed control method.

Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high‐frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super‐resolution. To alleviate this issue, the authors adopted a conditional GAN (cGAN) in this study. The cGAN discriminator attempted to guess whether the unknown high‐resolution (HR) image was produced by the generator with the aid of the original low‐resolution (LR) image. They propose a novel discriminator that only penalises at the scale of the patch and, thus, has relatively few parameters to train. The generator of cGAN is an encoder–decoder with skip connections to shuttle the shared low‐level information directly across the network. To better maintain the low‐frequency information and recover the high‐frequency information, they designed a generator loss function combining adversarial loss term and L1 loss term. The former term is beneficial to the synthesis of fine‐grained textures, while the latter is responsible for learning the overall structure of the LR input. The experiments revealed that the proposed method could generate HR images with richer details and less over‐smoothness.