A systematic study of biological SE systems from complexity and design perspectives

Abstract

Previous research has presented the concept of self-engineering (SE) systems that aim to identify and preserve system functions autonomously. Examples of self-engineering responses include self-healing, self-repair, self-adapting and self-reconfiguration. Biology already utilises many of these responses to repair and survive, greater understanding of complexity in these biological systems could improve future bioinspired designs. This paper provides a novel systematic evaluation of the complexity of SE biological systems. Eight biological self-engineering systems identified are evaluated using Axiomatic design and complexity. The key functional requirements and design parameters for each biological system are identified. Design matrices were used to highlight different types of complexity. A further evaluation of eight SE biological systems is performed using the SE complexity theory; nine experts and 23 students used the complexity theory to complete a ranking exercise. The results of the ranking were analysed and compared, with a final normalised mean plotted for each factor and biological system. From the analysis of both studies, proposed design rules are presented to help designers handle complexity while creating new self-engineering systems inspired by biology.