Browsing by Subject "optimal control"

Now showing 1 - 4 of 4
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    Coordinated Control Of The Modern Grid: High-Bandwidth Optimal Coordination, Inference, And Verification
    (2020-03) Lundstrom, Blake
    Modernization of electric grids worldwide continues in earnest, with increasing deployments of renewable and distributed energy resources (DERs); flexible loads with evolving usage patterns; and new control solutions in distribution networks. The convergence of these new technologies inherently increases the complexity of the power system; and maintaining safe, reliable, and efficient operation of modern grids relies on the successful integration of these many technologies together. Achieving successful modern grid control solutions requires not only the development of innovative distributed coordination techniques, but also methods to effectively and accurately test such complex solutions prior to field deployment and approaches to achieve cost-effective, high-bandwidth, inference capabilities in buildings and distribution grids. In the first part of this thesis, we make important contributions to the design of power hardware-in-the-loop (PHIL) simulations, an important tool for derisking modern grid technologies in a controlled laboratory setting. This technique allows actual at-power devices and systems to be interconnected in software simulations of complex, large-scale power system scenarios to evaluate their closed-loop interaction. We develop a novel approach to designing PHIL simulation interfaces that maximizes simulation bandwidth and accuracy by leveraging a modern control framework that explicitly considers objectives on accuracy and can automatically synthesize a single, optimal controller that meets these objectives while stabilizing the closed-loop system. This method improves upon common approaches to PHIL interface design that typically involve multiple steps of manual compensation and stabilization design that can result in interfaces that are stable but have suboptimal bandwidth and accuracy. The approach developed is general and can be applied to most PHIL system configurations. We also present a practical method and metrics for verifying the true accuracy of PHIL simulation results without relying on relative comparisons to potentially inaccurate models or previous simulation results. We demonstrate the accuracy evaluation method and show the improved performance achievable when using the optimal PHIL interface design approach in an experimental case study involving a 100-kVA battery inverter. The second part of this thesis develops a novel approach for high-frequency, multi-class nonintrusive load monitoring (NILM) that enables effective net-load monitoring capabilities with minimal additional equipment and cost. Relative to existing NILM work, the proposed solution operates at a faster timescale, providing accurate multiclass state predictions for each 60-Hz ac cycle without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states via classification but also their individual operating power levels via regression. The overall approach is validated using a test bed with eight residential appliances and is shown to have high accuracy, good scaling and generalization properties, and sufficient response time to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services. The third part of this thesis develops and experimentally demonstrates a first-of-its-kind hierarchical control solution for optimally dispatching thousands of deferrable loads and DERs across a distribution feeder to provide fast frequency response (FFR) within 500 ms to the bulk power system. This approach rapidly coordinates resources online after a frequency event occurs, allowing fast-changing, behind-the-meter resources to be incorporated and aggregate FFR power set points to be achieved more quickly and accurately than existing approaches. We also present a solution for determining the optimal amount of headroom to operate solar inverters with to minimize opportunity cost while ensuring the FFR response viability of a building with the inverter and deferrable loads. We develop practical algorithms for fast, cost-based optimal dispatch at multiple aggregation scales, establish their optimality, and demonstrate via simulation that they are faster than state-of-the-art, coordinated frequency response approaches. The entire control platform developed is implemented and experimentally verified using a unique PHIL demonstration, including more than 100 powered loads and DERs connected to a real-world distribution network model. Experimental results from multiple scenarios confirm that the optimal FFR dispatch approach scales well and can optimally coordinate more than 10,000 net-load resources across a distribution network while achieving hardware response times within 500 ms, which is not possible using existing optimal coordination approaches.
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    Learning of Unknown Environments in Goal-Directed Guidance and Navigation Tasks: Autonomous Systems and Humans
    (2017-12) Verma, Abhishek
    Guidance and navigation in unknown environments requires learning of the task environment simultaneous to path planning. Autonomous guidance in unknown environments requires a real-time integration of environment sensing, mapping, planning, trajectory generation, and tracking. For brute force optimal control, the spatial environment should be mapped accurately. The real-world environments are in general cluttered, complex, unknown, and uncertain. An accurate model of such environments requires to store an enormous amount of information and then that information has to be processed in optimal control formulation, which is not computationally cheap and efficient for online operations of autonomous guidance systems. On the contrary, humans and animals are in general able to navigate efficiently in unknown, complex, and cluttered environments. Like autonomous guidance systems, humans and animals also do not have unlimited information processing and sensing capacities due to their biological and physical constraints. Therefore, it is relevant to understand cognitive mechanisms that help humans learn and navigate efficiently in unknown environments. Such understanding can help to design planning algorithms that are computationally efficient as well as better understand how to improve human-machine interfaces in particular between operators and autonomous agents. This dissertation is organized in three parts: 1) computational investigation of environment learning in guidance and navigation (chapters 3 and 4), 2) investigation of human environment learning in guidance tasks (chapters 5 and 6), and 3) autonomous guidance framework based on a graph representation of environment using subgoals that are invariants in agent-environment interactions (chapter 7). In the first part, the dissertation presents a computational framework for learning autonomous guidance behavior in unknown or partially known environments. The learning framework uses a receding horizon trajectory optimization associated with a spatial value function (SVF). The SVF describes optimal (e.g. minimum time) guidance behavior represented as cost and velocity at any point in geographical space to reach a specified goal state. For guidance in unknown environments, a local SVF based on current vehicle state is updated online using environment data from onboard exteroceptive sensors. The proposed learning framework has the advantage in that it learns information directly relevant to the optimal guidance and control behavior enabling optimal trajectory planning in unknown or partially known environments. The learning framework is evaluated by measuring performance over successive runs in a 3-D indoor flight simulation. The test vehicle in the simulations is a Blade-Cx2 coaxial miniature helicopter. The environment is a priori unknown to the learning system. The dissertation investigates changes in performance, dynamic behavior, SVF, and control behavior in body frame, as a result of learning over successive runs. In the second part, the dissertation focuses on modeling and evaluating how a human operator learns an unknown task environment in goal-directed navigation tasks. Previous studies have showed that human pilots organize their guidance and perceptual behavior using the interaction patterns (IPs), i.e., invariants in their sensory-motor processes in interactions with the task space. However, previous studies were performed in known environments. In this dissertation, the concept of IPs is used to build a modeling and analysis framework to investigate human environment learning and decision-making in navigation of unknown environments. This approach emphasizes the agent dynamics (e.g., a vehicle controlled by a human operator), which is not typical in simultaneous navigation and environment learning studies. The framework is applied to analyze human data from simulated first-person guidance experiments in an obstacle field. Subjects were asked to perform multiple trials and find minimum-time routes between prespecified start and goal locations without priori knowledge of the environment. They used a joystick to control flight behavior and navigate in the environment. In the third part, the subgoal graph framework used to model and evaluate humans is extended to an autonomous guidance algorithm for navigation in unknown environments. The autonomous guidance framework based on subgoal graph is an improvement to the SVF based guidance and learning framework presented in the first part. The latter uses a grid representation of the environment, which is computationally costly in comparison to the graph based guidance model.
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    Live Long and Prosper: A Theory For Yield Differences Between Annual And Perennial Grains
    (2015-12) Barnes, Richard
    Several decades of breeding efforts to produce a high-yielding, long-lived herbaceous grain have not been successful. Yet, such a plant is conjectured to have many advantages over the annual grains society uses to feed itself --- advantages which are sorely needed as population growth and environmental limitations coalesce. This work lays a mathematical foundation based on techniques from dynamic optimization and optimal control theory for determining whether such a plant can ever exist. Ultimately, this work argues that high-yielding herbaceous perennial grains are possible.
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    A Variational Approach to H∞ Control with Transients
    (Institute of Electrical and Electronic Engineers, 1999-10) Lu, Wayne W.; Balas, Gary J.; Lee, E.B.
    This paper presents a variational approach to H∞ control with transients in the state feedback case. The approach here provides a precise description with equality, instead of inequality, in the necessary and sufficient conditions for the existence of a linear controller. Furthermore, the solution existence and uniqueness are proved in terms of certain properties of the indefinite Riccati equations derived in this paper. The linear time-variant (LTV) plant on finite horizon is considered first, and then the results are extended to the linear time-invariant (LTIV) plant on the infinite horizon. By this approach, it can be directly concluded that only suboptimal H∞ state feedback control can be achieved in an input–output point of view and that the performance measure γ(μ1/2 used in this paper) is a strict upper bound.