Biblio

Since the late 1990s the sales of processors targeted for embedded systems has exceeded sales for the PC market. Some embedded systems tightly link the computing resources to the physical world. Such systems are called cyber-physical systems. Autonomous cyber-physical systems often have safety-critical missions, which means they must be fault tolerant. Unfortunately fault recovery options are limited; adapting the physical system behavior may be the only viable option. Consequently, autonomous cyber-physical systems are a class of adaptive systems. The evolvable hardware field has developed a number of techniques that should prove to be useful for designing cyber-physical systems although work along those lines has only recently begun. In this paper we provide an overview of cyber-physical systems and then describe how two evolvable hardware techniques can be used to adapt the physical system behavior in real-time. The goal is to introduce cyber-physical systems to the evolvable hardware community and encourage those researchers to begin working in this emerging field.

This paper documents and discusses the design of a low-cost Flapping-Wing Micro Air Vehicle (FW-MAV) designed to be easy to fabricate using readily available materials and equipment. Basic theory of operation as well as the rationale underlying various design decisions will be provided. Using this paper, it should be possible for readers to construct their own devices quickly and at little expense.

In previous work, the viability of split-cycle constant-period frequency modulation for controlling two degrees of freedom of flapping wing micro air vehicle has been demonstrated. Though the proposed wing control system was made compact and self-sufficient to be deployed on the vehicle, it was not built for on-the-fly configurability of all the split-cycle control's parameters. Further the system had limited external communication capabilities that rendered it inappropriate for its integration into a higher level research framework to analyze and validate motion controllers in flapping vehicles. In this paper, an improved control system has been proposed that could addresses the on-the-fly configurability issue and provide an improved external communication capabilities, hence the wing control system could be seamlessly integrated in a research framework for analyzing and validating motion controllers for flapping wing vehicles.

Machine-learning and soft computation methods are often used to adapt and modify control systems for robotic, aerospace, and other electromechanical systems. Most often, those who use such methods of self-adaptation focus on issues related to efficacy of the solutions produced and efficiency of the computational methods harnessed to create them. Considered far less often are the effects self-adaptation on Verification and Validation (V{&}V) of the systems in which they are used. Simply observing that a broken robotic or aerospace system seems to have been repaired is often not enough. Since self-adaptation can severely distort the relationships among system components, many V{&}V methods can quickly become useless. This paper will focus on a method by which one can interleave machine-learning and model consistency checks to not only improve system performance, but also to identify how those improvements modify the relationship between the system and its underlying model. Armed with such knowledge, it becomes possible to update the underlying model to maintain consistency between the real and modeled systems. We will focus on a specific application of this idea to maintaining model consistency for a simulated Flapping-Wing Micro Air Vehicle that uses machine learning to compensate for wing damage incurred while in flight. We will demonstrate that our method can detect the nature of the wing damage and update the underlying vehicle model to better reflect the operation of the system after learning. The paper will conclude with a discussion of potential future applications, including generalizing the technique to other vehicles and automating the generation of model consistency-testing hypotheses.