Reconfiguration and bifurcation in flight controls

Numerous aviation accidents have been caused by stuck control surfaces. In most cases the impaired aircraft has sufficient redundancy to reconfigure the flight. However, the actions that the pilot needs to make could be counter intuitive, demanding and complicated. This is due to the drastic changes in the system's dynamics thatare caused by the nonlinearities, the loss of control authority and the disturbance imposed by the stuck surface. The reconfiguration of the flight laws will alleviate the work load on the crew and give them a better leeway to safely land the aircraft. The fault tolerant scheme that is adopted here is a multiple model one with a finite number of reconfigured controllers. Each reconfigured controller consists of a nonlinear output regulator and a constant gain nonlinear observer. The guidelines available for designing the nominal stabilizer are not appropriate for the reconfigured systems.The ability of the control law to reconfigure the aircraft is limited by saturation of the control surfaces, bifurcation points and stability limits. Identifying and characterizing these limitations is the first step in systematically improving the fault tolerant design. The computational results were obtained using a continuation method based on the Newton-Raphson and Newton-Raphson-Seydel methods. The numerous subtleties in employing these tools, when bifurcation points are clustered together, when many eigenvalues are near the origin or when the eigenvalues nearest the origin are complex, are addressed in this work. The reconfigured controller design for all possible single surface failures and the bifurcation analysis of the nominal and reconfigured systems was carried out on a real aircraft, namely the F-16. This was facilitated by the development of a unique, high fidelity, six degree of freedom, F-16 model.Ph.D., Mechanical Engineering -- Drexel University, 200

Semantic Networks for Hybrid Processes.

Simulation models are often used in parallel with a physical system to facilitate control, diagnosis and monitoring. Model based methods for control, diagnosis and monitoring form the basis for the popular sobriquets `intelligent', `smart' or `cyber-physical'. We refer to a configuration where a model and a physical system are run in parallel as a emph{hybrid process}. Discrepancies between the model and the process may be caused by a fault in the process or an error in the model. In this work we focus on correcting modeling errors and provide methods to correct or update the model when a discrepancy is observed between a model and process operating in parallel. We then show that some of the methods developed for model adaptation and diagnosis can be used for control systems design. There are five main contributions. The first contribution is an analysis of the practical considerations and limitations of a networked implementation of a hybrid process. The analysis considers both the delay and jitter in a packet switching network as well as limits on the accuracy of clocks used to synchronize the model and process. The second contribution is a semantic representation of hybrid processes which enables improvements to the accuracy and scope of algorithms used to update the model. We demonstrate how model uncertainty can be balanced against signal uncertainty and how the structure of interconnections between model components can be automatically reconfigured if needed. The third contribution is a diagnostic approach to isolate model components responsible for a discrepancy between model and process, for a structure preserving realization of a system of ODEs. The fourth contribution is an extension of the diagnostic strategy to include larger graphs with cycles, model uncertainty and measurement noise. The method uses graph theoretic tools to simplify the graph and make the problem more tractable and robust to noise. The fifth contribution is a simulation of a distributed control system to illustrate our contributions. Using a coordinated network of electric vehicle charging stations as an example, a consensus based decentralized charging policy is implemented using semantic modeling and declarative descriptions of the interconnection network.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99903/1/danand_1.pd