This Cyber Physical Systems (CPS) project will develop novel sensing, actuation, and embedded computing technologies that allow civil infrastructures to be responsive, resilient and adaptive in the face of dynamic loads. Such technologies require delivery of electrical power, typically either via an external power grid, or through the use of battery storage. However, grid power may be unreliable during extreme loading events, and batteries must be periodically recharged or replaced. The novelty of the technologies developed in this project is that they power themselves, by storing and reusing energy injected into the infrastructure by external loads. The project focuses on three applications: (1) urban stormwater networks that actively control water levels to prevent flooding, using power generated from the hydrologic flows, (2) buildings that actively control their deformations during earthquakes and high winds, using power generated from vibrations, and (3) ocean desalination systems that actively control pumping rates, using power generated from waves. The project contains an experimental campaign for each application. It also contains an analytical component, focused on the development of control algorithms to maximize the performance of the technologies. Educational outreach activities include class modules and research experiences for undergraduate and graduate students, as well as a workshop for high school students. Control algorithms for self-powered infrastructures must explicitly optimize the balance between power generation and performance objectives. This project will innovate new Model Predictive Control algorithms for self-powered infrastructure technologies, such that they achieve the best performance possible while not running out of energy. These algorithms will be validated experimentally, for all three applications. There is presently no existing theory for optimal control of self-powered systems that is scalable to large and complex systems such as the ones under consideration. The research to be conducted here will augment recent advances in Model Predictive Control theory, to result in a new body of knowledge in this area. Challenges include: (1) innovation of optimization algorithms that can contend with the inherent nonconvexity of optimal self-powered control problems; (2) development of effective techniques for handling the stochastic nature of the dynamics for the target applications; (3) synthesis of controllers that are computationally tractable, but which also optimally compensate for the complex transmission losses and constraints in the power trains; (4) the derivation of systematic techniques for ensuring the robustness of the controllers, to uncertainties in the system model and disturbances.