Introducing KUGE: A Simultaneous Control Co-Design Architecture and its Application to Aerial Robotics Development

Abstract

The increasing complexity of tasks performed by hybrid aerial robotic systems, such as tail-sitters, demands a more integrated approach to their design. Traditional sequential design methods fall short because they separate the control system design from the conceptual design, limiting the poten-tial for discovering coupled solutions. This disjointed process constrains the design space, making it difficult to optimize both the control performance and system dynamics simultaneously. In response to this limitation, there has been growing interest in mission-specific dynamic design procedures, which aim to address specific operational challenges by integrating control and design early in the development process. The multi-disciplinary approach of control co-design (CCD) expands the design space by solving control and system design problems concurrently. The recently introduced DAIMYO framework demonstrated that combining multi-fidelity modelling with a nested CCD approach can tackle the situ-to-reel gap. However, DAIMYO's reliance on Bayesian optimization to account for the computational cost increase of a nested formulation limits its scalability. To address these issues, we propose KUGE, a simultaneous CCD strategy that reduces computational complexity and overcomes dimensionality restrictions through a combined effort of stochastic optimization and Gaussian processes. We validate the effectiveness of KUGE by applying it to the dynamic design of a tail-sitter, showing that it is competitive with the DAIMYO architecture while offering greater computational efficiency.