Diagnosability-optimization design of fixed-wing unmanned aerial vehicle based on causal matching and tree seed algorithm

Abstract

With mission intensity and difficulty escalating, the future market demand for UAV reliability and safety grows dramatically. So, this paper proposes an optimal design strategy based on causal matching and the Tree Seed Algorithm (TSA) based on structural analysis. First, a novel algorithm is designed for finding the minimum set of consistency relations for diagnosability analysis. The complexity of this algorithm is at a polynomial level, which is a significant improvement over previous algorithms with exponential complexity. A causal consistency search algorithm is also innovatively proposed for causal diagnosability analysis considering the causal constraints of the dynamic variables. Secondly, a diagnosability optimization strategy based on TSA is designed to balance the diagnosability requirements and the design cost of consistency relations. This strategy can satisfy the system’s diagnosability demand under different causal constraints with minimum consistency relations. Finally, a fixed-wing UAV model is established to analyze the diagnosability under different causal constraints qualitatively. Based on the TSA, consistency relations with the minimum integrated diagnosis cost and the best diagnosis performance are preferred.