A fan-powered tailsitter is capable of operating in both rotary-wing and fixed-wing flight modes. The transition between these modes is critical due to strong disturbances and considerable control complexity. This article investigates a predefined-time stability tracking control problem for tailsitters subject to parameter uncertainties and external disturbances. Based on the second-order dynamic model and high-order fully actuated (HOFA) system approach, a high-order robust controller is first developed to address the limitations of existing mode transitions, particularly in terms of control accuracy and disturbance suppression capabilities. On this basis, a novel predefined-time HOFA scheme is proposed by introducing adjustable parameters, which enables the system states to converge into a small neighborhood of the desired equilibrium within a prescribed time, while providing flexible tuning of the convergence time to adapt to varying mission and environmental requirements. Theoretical analysis and numerical simulations demonstrate that the proposed scheme achieves enhanced control accuracy, faster convergence, and improved robustness compared with conventional approaches. In contrast to existing approaches, the proposed HOFA-based predefined-time framework allows explicit tuning of the convergence time and provides robustness guarantees under parameter uncertainties, an aspect that has not been sufficiently addressed in the current literature.

Image rendering from line drawings is vital in design and image generation technologies reduce costs, yet professional line drawings demand preserving complex details. Text prompts struggle with accuracy, and image translation struggles with consistency and fine-grained control. We present LineArt, a framework that transfers complex appearance onto detailed design drawings, facilitating design and artistic creation. It generates high-fidelity appearance while preserving structural accuracy by simulating hierarchical visual cognition and integrating human artistic experience to guide the diffusion process. LineArt overcomes the limitations of current methods in terms of difficulty in fine-grained control and style degradation in design drawings. It requires no precise 3D modeling, physical property specifications, or network training, making it more convenient for design tasks. LineArt consists of two stages: a multi-frequency lines fusion module to supplement the input design drawing with detailed structural information and a two-part painting process for Base Layer Shaping and Surface Layer Coloring. We also present a new design drawing dataset, ProLines, for evaluation. The experiments show that LineArt performs better in accuracy, realism, and material precision compared to SOTAs. Project page: https://meaoxixi.github.io/LineArt/.