In this thesis, robust nonlinear control methods are developed, which are rigorously proven to achieve reliable control of fluid flow systems under uncertain, time-varying operating conditions and actuator model uncertainty. Further, to address the practical control design challenges resulting from sensor limitations, this thesis research will investigate and develop new methods of sliding mode estimation, which are shown to achieve finite-time state estimation for systems with limited onboard sensing capabilities. The specific contributions presented in this thesis include: 1) the application of proper orthogonal decomposition (POD)-based model order reduction techniques to develop simplified, control-oriented mathematical models of actuated fluid flow dynamic systems; 2) the rigorous development of nonlinear closed-loop active flow control techniques to achieve asymptotic regulation of fluid flow velocity fields; 3) the design of novel sliding mode estimation and control methods to regulate fluid flow velocity fields in the presence of actuator uncertainty; 4) the design of a nonlinear control method that achieves simultaneous fluid flow velocity control and LCO suppression in a flexible airfoil; and 5) the analysis of a discontinuous hierarchical sliding mode estimation method using a differential inclusions-based technique.

]]>The proposed system will present a platform that leverages modernize blockchain called Blocks’ Network. The system is taking into consideration the issues related to privacy and confidentiality from the client-side model, and scalability and latency issues from the blockchain technology side. Blocks’ network is a public and a permissioned network that use a multi-dimensional hash to generate multiple chains for the purpose of a systematic approach.

The proposed platform is an assembly point for users to create a decentralized network using P2P protocols. The system has high data flow due to frequent use by participants (for example, the use of the Internet). Besides, the system will store all traffic of the network overtly via Blocks’ Network.

]]>The system dynamics describe the relative motion of an arbitrary number of maneuvering (chaser) spacecraft about a single non-cooperative resident-space-object (RSO). The chaser spacecraft motion is constrained in terms of the 1) collision bounds of the RSO, 2) maximum fuel usage, 3) eclipse avoidance, and 4) optical sensor field of view restrictions. When more than one chaser is present, additional constraints include 1) collision avoidance between formation members, and 2) formation longevity via fuel usage balancing.

Depending on the type of planetary orbit, quasi-circular or elliptic, the relative motion dynamics are approximated using a linear time-invariant or a linear time-varying system, respectively. The proposed method uses two distinct parameterizations corresponding to each system type to reduce the optimization problem from 12 to 2 variables in Cartesian space, thus simplifying an otherwise intractable optimization problem.

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