Biblio

Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper we describe the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details on the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

To date, work in evolvable and adaptive hardware (EAH) has been largely isolated from primary inclusion into larger design processes. Almost without exception, EAH efforts are aimed at creating systems whole cloth, creating drop-in replacements for existing components of a larger design, or creating after-the-fact fixes for designs found to be deficient. This paper will discuss early efforts in integrating EAH methods into the design of a controller for a flapping-wing micro air vehicle (FWMAV). The FWMAV project is extensive, multidisciplinary, and on going. Because EAH methods were in consideration during its earliest design stages, this project provides a rich environment in which to explore means of effectively combining EAH and traditional design methodologies. In addition to providing a concrete EAH design that addresses potential problems with FWMAV flight in a unique way, this paper will also provide a provisional list of EAH design integration principles, drawn from our experiences to date.