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Boddhu, Sanjay K., Botha, Hermanus V., Perseghetti, Ben M., Gallagher, John C..  2014.  Improved Control System for Analyzing and Validating Motion Controllers for Flapping Wing Vehicles. Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications. :557–567.

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.

Botha, Hermanus V., Boddhu, Sanjay K., McCurdy, Helena B., Gallagher, John C., Matson, Eric T., Kim, Yongho.  2015.  A Research Platform for Flapping Wing Micro Air Vehicle Control Study. Robot Intelligence Technology and Applications 3: Results from the 3rd International Conference on Robot Intelligence Technology and Applications. :135–150.

The split-cycle constant-period frequency modulation for flapping wing micro air vehicle control in two degrees of freedom has been proposed and its theoretical viability has been demonstrated in previous work. Further consecutive work on developing the split-cycle based physical control system has been targeted towards providing on-the-fly configurability of all the theoretically possible split-cycle wing control parameters with high fidelity on a physical Flapping Wing Micro Air Vehicle (FWMAV). Extending the physical vehicle and wing-level control modules developed previously, this paper provides the details of the FWMAV platform, that has been designed and assembled to aid other researchers interested in the design, development and analysis of high level flapping flight controllers. Additionally, besides the physical vehicle and the configurable control module, the platform provides numerous external communication access capabilities to conduct and validate various sensor fusion study for flapping flight control.

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Gallagher, John C., Oppenheimer, Michael W..  2012.  An Improved Evolvable Oscillator and Basis Function Set for Control of an Insect-Scale Flapping-Wing Micro Air Vehicle. Journal of Computer Science and Technology. 27:966–978.

This paper introduces an improved evolvable and adaptive hardware oscillator design capable of supporting adaptation intended to restore control precision in damaged or imperfectly manufactured insect-scale flapping-wing micro air vehicles. It will also present preliminary experimental results demonstrating that previously used basis function sets may have been too large and that significantly improved learning times may be achieved by judiciously culling the oscillator search space. The paper will conclude with a discussion of the application of this adaptive, evolvable oscillator to full vehicle control as well as the consideration of longer term goals and requirements.

Gallagher, John C., Humphrey, Laura R., Matson, Eric.  2014.  Maintaining Model Consistency during In-Flight Adaptation in a Flapping-Wing Micro Air Vehicle. Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications. :517–530.

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.

Goppert, James, Gallagher, John C., Hwang, Inseok, Matson, Eric.  2014.  Model Checking of a Flapping-Wing Mirco-Air-Vehicle Trajectory Tracking Controller Subject to Disturbances. Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications. :531–543.

This paper proposes a model checking method for a trajectory tracking controller for a flapping wing micro-air-vehicle (MAV) under disturbance. Due to the coupling of the continuous vehicle dynamics and the discrete guidance laws, the system is a hybrid system. Existing hybrid model checkers approximate the model by partitioning the continuous state space into invariant regions (flow pipes) through the use of reachable set computations. There are currently no efficient methods for accounting for unknown disturbances to the system. Neglecting disturbances for the trajectory tracking problem underestimates the reachable set and can fail to detect when the system would reach an unsafe condition. For linear systems, we propose the use of the H-infinity norm to augment the flow pipes and account for disturbances. We show that dynamic inversion can be coupled with our method to address the nonlinearities in the flapping-wing control system.

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Perseghetti, Benjamin M., Roll, Jesse A., Gallagher, John C..  2014.  Design Constraints of a Minimally Actuated Four Bar Linkage Flapping-Wing Micro Air Vehicle. Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications. :545–555.

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.

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Sam, Monica, Boddhu, Sanjay K., Duncan, Kayleigh, Botha, Hermanus V., Gallagher, John C..  2016.  Improving In-Flight Learning in a Flapping Wing Micro Air Vehicle. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR). 4:14.