A major goal of Intelligent Control in advanced aircraft and spacecraft is to achieve high performance with increased reliability, availability, component durability, and maintainability. The current state-of-the-art in Intelligent Control Systems focuses on improving performance and diagnostic capabilities under constraints that often do not adequately represent the dynamic properties of the materials. The reason is that the traditional design is based upon the assumption of conventional materials with invariant characteristics. In view of high performance requirements and availability of improved materials, the lack of appropriate knowledge about the properties of these materials will lead to either less than achievable performance due to overly conservative design, or over-straining of the structure leading to unexpected failures and drastic reduction of the service life. The key idea of the research reported in this paper is that a significant improvement in service life could be achieved by a small reduction in the system dynamic performance. This requires augmentation of the current system-theoretic and AI-based techniques for synthesis of decision and control laws with governing equations and inequality constraints that would model the properties of the materials for the purpose of damage representation and failure prognosis. The major challenge in this research is to extract the information from the material properties and then utilize this information in a mathematical form for synthesizing intelligent control and diagnostic systems.