Christopher Vermillion

Abstract This paper presents a control-oriented dynamic model, controller, and closed-loop mobility characterization for the first wind-powered spherical rover capable of net upwind motion. This device, termed the Spherical Sailing Omnidirectional Rover (SSailOR), incorporates design features within a spherical, terrestrial rover that mimic the role that a centerboard (or keel) and lifting sails play in allowing net upwind motion for sailboats. Specifically, a traction hoop enables significant lateral resistance, thereby providing a nonholonomic constraint in the direction of travel. Lifting sails enables net thrust even when traveling significantly upwind, while also providing heading control. While providing unique capabilities, the SSailOR gives rise to a complex design and control space, where careful model-based design and control are necessary to ensure that the SSailOR can simultaneously (i) make net upwind progress, (ii) respond quickly to wind speed/direction changes, (iii) limit heel angle, and (iv) control its heading. To simultaneously address these challenges, we first present a control-oriented dynamic model. This is followed by the presentation of a combined heading and heel angle controller. Finally, with the dynamic model and control structure in place, we present a detailed closed-loop Pareto analysis, which illustrates the tradeoff between transient and steady-state performance, along with the design features that favor one modality of performance over another.

Abstract Renewable energy systems are subject to uncertainty arising both from modeling simplifications/imperfections and a stochastic environment. To maximize energy production under this uncertainty, real-time plant and controller adaptability can be introduced. While the inclusion of plant and controller adaptability has been demonstrated to enhance the energetic performance of both wind and marine energy systems, this comes at an economic cost, due to the need for additional actuators and adaptive control software development. In this work, a co-design framework is presented to identify the system design, model refinement technique, and degree of plant and controller adaptability that minimize the expected levelized cost of energy (LCOE) of a renewable energy system. This framework is then applied to a detailed case study involving an installation of marine hydrokinetic (MHK) kites, using a detailed cost model and a computationally efficient surrogate model to estimate energetic performance. The study resulted in identified LCOE-optimal levels of plant and controller adaptability, in addition to an optimal level of model refinement following the plant freeze date (recognizing that this model refinement comes at a development cost). To fully realize the range of adaptation built into the design of the optimized kite system, a real-time extremum-seeking-based adaptation strategy is presented. Through dynamic simulation results, we show that the energetic performance of the kite converges to the predictions generated by the surrogate model, thus validating the use of the surrogate model in the co-design optimization.

Abstract Large-scale wind energy-harvesting systems operate in highly variable environments and face long design and manufacturing cycles that often require design freezes before high-certainty modeling is possible. Both challenges can be addressed by incorporating real-time plant and controller adaptability, recognizing that adaptability comes at a cost. The proposed co-design framework aims to maximize the lifetime expected profit of a wind energy system (though its mathematical underpinnings are generalizable to other energy-harvesting devices), accounting for a low-complexity surrogate model of system performance, a statistical model of the environment, a characterization of how modeling uncertainty diminishes over the design cycle, and cost models that consider the price of adaptability and engineering labor. By jointly considering plant and controller adaptability, this framework trades off high-cost/high-reward plant adaptability with lower-cost/lower-reward controller adaptability. This co-design framework is coupled with an online control strategy that performs real-time adaptation under constraints. To evaluate this approach, we focus on the segmented ultralight morphing rotor (SUMR) wind turbine. Applying the co-design framework to the SUMR using the aforementioned surrogate model, the expected lifetime profit increases by 16.7% when adaptability is optimized. To validate the surrogate model, a 24-h dynamic simulation was conducted using the optimized design. The SUMR system with an online adaptive controller generated 4.4% more energy than the system with a nonadaptive controller, demonstrating the impact of adaptability on performance. Dynamic simulation predictions closely match those of the surrogate model used in the co-design optimization, further validating the approach.

This brief presents the development of an online iterative learning technique for the optimization of both the flight path parameters and rotor control parameters for an underwater energy-harvesting kite. In this technique, a coupled control parameter space is explored through a switching exploration strategy, whereby only a subset of the total system parameters is adapted at each iteration. This formulation, termed switching economic iterative learning control (se-ILC), seeks to intelligently explore this coupled design space to rapidly converge near the global optimum. This work examines the high-level control of an underwater energy-harvesting kite as an application example of se-ILC. First, we establish generalized performance bounds for economic iterative learning control (e-ILC) in the presence of a time-varying environment subjected to parametric uncertainties. Leveraging an existing dynamic model, we then examine the performance of the se-ILC formulation applied to this specific application example, exploring the coupled path and rotor angular velocity parameter spaces. We demonstrate that the se-ILC strategy enables convergence to within tighter bounds (compared with standard e-ILC) of the true optimum in the presence of an imperfect performance characterization.

In this work, a methodology for controlling the flight of an underwater energy-harvesting kite, termed enhanced orientation-based control, is presented. This control technique is shown to perform comparably to more complex, hierarchical path-following control approaches that rely upon expensive and unreliable localization sensors while performing significantly better than simple orientation-based controllers that possess a comparable degree of complexity. The periodic closed-loop stability of a kite utilizing the proposed controller is validated in a low-order simulation framework. From there, the performance of the proposed controller is benchmarked against established control techniques via a medium-fidelity simulation environment. Finally, the efficacy of the proposed controller design is demonstrated experimentally based on two testing results on a scaled prototype kite.

Marine hydrokinetic kites have the potential to efficiently exploit energy from the ocean by extracting energy from currents. To ensure efficiency in energy harvesting, these devices must be optimally sized for the operating conditions they will be deployed under. Kites experience significant structural loading on their wings due to their high flight speeds. It follows that the allowable sizing of these kites is driven by a structural constraint on the wing's deflection, which is dictated by the lift forces on the wing, and the need to satisfy neutral buoyancy, which limits the mass and rigidity of the wing. For this work, a thorough study of how the structural constraint activity changes under different operating conditions, and how this affects design optimization feasibility was performed. Additionally, the feasible design range was studied for various kite payloads. The results indicate that as the wing span increases, the structural constraint activity increases. Moreover, as flow speed increases and the distance from the kite's base station to centerpoint of the kite's path decreases, the structural constraint is unable to be satisfied while meeting the neutral buoyancy requirement. Furthermore, the characterization of when the optimization becomes infeasible as the payload changes was studied.

Planning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot’s limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0, 1]. The clarity dynamics allow us to capture information decay due to a lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study. [Code]<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup>[Video]<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup>

Characterizing evolving ocean environments is important to scientific, renewable energy, and military applications. However, performing meaningful characterizations of these resources is complicated by their spatiotemporal evolution and partial observability. In this work, we specifically consider the use of an autonomous underwater vehicle (AUV) with a deployable energy-harvesting kite that enables persistent missions. When the AUV parks itself on the seabed, the kite can deploy, harvesting significant amounts of energy through periodic figure-8 flight. Focusing on a Gulf Stream observational mission, we present a persistent planning algorithm that fuses Gaussian process (GP) modeling with model predictive control (MPC) to optimize AUV charging times to maximize the informativeness of the mission. Based on simulation studies using a mid-Atlantic bight, south Atlantic bight regional ocean model (MAB-SAB-ROM), we demonstrate a 20% reduction in the time required to traverse a given section of the Gulf Stream, which leads to a significant reduction in prediction error.

Planning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot's limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models, or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0,1]. The clarity dynamics allows us to capture information decay due to lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study.

This project focused on the modeling, device design, control system design, and progressive experimental validation of an energy-harvesting underwater kite harvesting energy through cyclic spooling motion. This adds to the portfolio of the DoE Water Power Technologies Office has resulted in several key outcomes that will further the development of energy-harvesting kites, as detailed in the report.