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J. C. Gallagher, E. T. Matson, G. W. Greenwood.  2013.  On the implications of plug-and-learn adaptive hardware components toward a cyberphysical systems perspective on evolvable and adaptive hardware. 2013 IEEE International Conference on Evolvable Systems (ICES). :59-65.

Evolvable and Adaptive Hardware (EAH) Systems have been a subject of study for about two decades. This paper argues that viewing EAH devices in isolation from the larger systems in which they serve as components is somewhat dangerous in that EAH devices can subvert the design hierarchies upon which designers base verification and validation efforts. The paper proposes augmenting EAH components with additional machinery to enable the application of model-checking and related Cyber-Physical Systems techniques to extract evolving intra-module relationships for formal verification and validation purposes.

J. C. Gallagher, S. Boddhu, E. Matson, G. Greenwood.  2014.  Improvements to Evolutionary Model Consistency Checking for a Flapping-Wing Micro Air Vehicle. 2014 IEEE International Conference on Evolvable Systems. :203-210.

Evolutionary Computation has been suggested as a means of providing ongoing adaptation of robot controllers. Most often, using Evolutionary Computation to that end focuses on recovery of acceptable robot performance with less attention given to diagnosing the nature of the failure that necessitated the adaptation. In previous work, we introduced the concept of Evolutionary Model Consistency Checking in which candidate robot controller evaluations were dual-purposed for both evolving control solutions and extracting robot fault diagnoses. In that less developed work, we could only detect single wing damage faults in a simulated Flapping Wing Micro Air Vehicle. We now extend the method to enable detection and diagnosis of both single wing and dual wing faults. This paper explains those extensions, demonstrates their efficacy via simulation studies, and provides discussion on the possibility of augmenting EC adaptation by exploiting extracted fault diagnoses to speed EC search.

J. C. Gallagher, M. Sam, S. Boddhu, E. T. Matson, G. Greenwood.  2016.  Drag force fault extension to evolutionary model consistency checking for a flapping-wing micro air vehicle. 2016 IEEE Congress on Evolutionary Computation (CEC). :3961-3968.

Previously, we introduced Evolutionary Model Consistency Checking (EMCC) as an adjunct to Evolvable and Adaptive Hardware (EAH) methods. The core idea was to dual-purpose objective function evaluations to simultaneously enable EA search of hardware configurations while simultaneously enabling a model-based inference of the nature of the damage that necessitated the hardware adaptation. We demonstrated the efficacy of this method by modifying a pair of EAH oscillators inside a simulated Flapping-Wing Micro Air Vehicle (FW-MAV). In that work, we were able to show that one could, while online in normal service, evolve wing gait patterns that corrected altitude control errors cause by mechanical wing damage while simultaneously determining, with high precision, what the wing lift force deficits that necessitated the adaptation. In this work, we extend the method to be able to also determine wing drag force deficits. Further, we infer the now extended set of four unknown damage estimates without substantially increasing the number of objective function evaluations required. In this paper we will provide the outlines of a formal derivation of the new inference method plus experimental validation of efficacy. The paper will conclude with commentary on several practical issues, including better containment of estimation error by introducing more in-flight learning trials and why one might argue that these techniques could eventually be used on a true free-flying flapping wing vehicle.

J. C. Gallagher, E. T. Matson, J. Goppert.  2017.  A Provisional Approach to Maintaining Verification and Validation Capability in Self-Adapting Robots. 2017 First IEEE International Conference on Robotic Computing (IRC). :382-388.

Cyber Physical Systems (CPS) are composed of multiple physical and computing components that are deeply intertwined, operate on differing spatial and temporal scales, and interact with one another in fluid, context dependent, manners. Cyber Physical Systems often include smart components that use local adaptation to improve whole system performance or to provide damage response. Evolvable and Adaptive Hardware (EAH) components, at least conceptually, are often represented as an enabling technology for such smart components. This paper will outline one approach to applying CPS thinking to better address a growing need to address Verification and Validation (V&V) questions related to the use of EAH smart components. It will argue that, perhaps fortuitously, the very adaptations EAH smart components employ for performance improvement may also be employed to maintain V&V capability.

J. C. Gallagher, D. B. Doman, M. W. Oppenheimer.  2012.  The Technology of the Gaps: An Evolvable Hardware Synthesized Oscillator for the Control of a Flapping-Wing Micro Air Vehicle. IEEE Transactions on Evolutionary Computation. 16:753-768.

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.

J. Chai, R.G. Sanfelice.  2015.  On Notions and Sufficient Conditions for Forward Invariance of Sets for Hybrid Dynamical Systems. Proceedings of the 54th IEEE Conference on Decision and Control. :2869-2874.
J. Chai, R. G. Sanfelice.  2016.  Results on Feedback Design for Forward Invariance of Sets in Hybrid Dynamical Systems. Proceedings of the 55th IEEE Conference on Decision and Control. :622–627.
J. Chai, R. G. Sanfelice.  2017.  On Robust Forward Invariance of Sets for Hybrid Dynamical Systems. Proceedings of the American Control Conference. :1199–1204.
J. Chai, P. Casau, R. G. Sanfelice.  2017.  Analysis of Event-triggered Control Algorithms using Hybrid Systems Tools. To appear in Proceedings of the IEEE Conference on Decision and Control.
J. Chai, R. G. Sanfelice.  2015.  Hybrid Feedback Control Methods for Robust and Global Power Conversion. Proceedings of the 5th Analysis and Design of Hybrid Systems. :298-303.
J. Duan, M. Y. Chow.  2017.  Data Integrity Attack on Consensus-based Distributed Energy Management Algorithm. 2017 IEEE Power and Energy Society General Meeting (PESGM). :1-5.
J. Duan, M. Y. Chow.  2017.  Data Integrity Attack on Consensus-based Load Shedding Algorithm for Power Systems. IECON 2017 - 43nd Annual Conference of the IEEE Industrial Electronics Society. :1-6.
J. Huang, G. Xing, J. Niu, S. Lin.  2015.  CodeRepair: PHY-layer partial packet recovery without the pain. 2015 IEEE Conference on Computer Communications (INFOCOM). :1463-1471.
J. Kaur, N. R. Chaudhuri.  2016.  Challenges of model reduction in modern power grid with wind generation. 2016 North American Power Symposium (NAPS). :1-6.
J. Kaur, N. R. Chaudhuri.  2017.  MIMO Model Reduction of Modern Power Grids with Wind Generation: Some New Findings. 2017 IEEE Power and Energy Society General Meeting (PESGM). :1-5.
J. Khalife, S. Ragothaman, Z. Kassas.  2017.  Pose estimation with lidar odometry and cellular pseudoranges. Proceedings of IEEE Intelligent Vehicles Symposium. :1722–1727.
J. Mike McHugh, Janusz Konrad, Venkatesh Saligrama, Pierre{-}Marc Jodoin, David A. Castañón.  2008.  Motion detection with false discovery rate control. Proceedings of the International Conference on Image Processing, {ICIP} 2008, October 12-15, 2008, San Diego, California, {USA}. :873–876.