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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Vector-level feedforward control of LPBF melt pool area using a physics-based thermal model

Laser powder bed fusion (LPBF) is an additive manufacturing technique that has gained popularity thanks to its ability to produce geometrically complex, fully dense metal parts. However, these parts are prone to internal defects and geometric inaccuracies, stemming in part from variations in the melt pool. Here, this paper proposes a novel vector-level feedforward control framework for regulating melt pool area in LPBF. By decoupling part-scale thermal behavior from small-scale melt pool physics, the controller provides a scale-agnostic prediction of melt pool area and efficient optimization over it. This is done by operating on two coupled lightweight models: a finite-difference thermal model that efficiently captures vector-level temperature fields and a reduced-order, analytical melt pool model. Each model is calibrated separately with minimal single-track and 2D experiments, and the framework is validated on a complex 3D geometry in both Inconel 718 and 316L stainless steel. Results showed that feedforward vector-level laser power scheduling reduced geometric inaccuracy in key dimensions by 62%, overall porosity by 16.5%, and photodiode root-mean-squared deviation by 38.5% on average. Overall, this modular, data-efficient approach demonstrates that proactively compensating for known thermal effects can significantly improve part quality while remaining computationally efficient and readily extensible to other materials and machines.

Additive manufacturing↗
DOE OSTI2026

Distributed optimization and control for autonomous distributed energy resource power dispatch and frequency regulation considering communication failures

Distributed energy resources (DERs) can significantly enhance grid flexibility by supplying power and secondary frequency regulation (SFR) services. Yet, their large number and dispersed nature limit the effectiveness of traditional centralized power dispatch and frequency control schemes, and expose the system to single-point communication failures. This paper presents a unified distributed optimization and control framework for DER power dispatch and SFR provision. A transmission-loss-aware distributed direct current optimal power flow is first formulated to optimize DER power dispatch and reserve scheduling. On top of this, a fully distributed control scheme for DER autonomous SFR provision is developed to allocate automatic generation control signals cost-effectively while ensuring robustness to communication link disruptions. The framework is validated on IEEE 14-bus and 39-bus transmission systems with large-scale distribution feeders using transmission-distribution dynamic co-simulation, demonstrating reliable and autonomous DER power dispatch and SFR provision under diverse operating conditions and communication contingencies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

EVSE DERMS Controls [SWR-26-010]

An MQTT (Message Queuing Telemetry Transport) and OCPP (Open Charge Point Protocol) based remote smart charging controller framework for AC Electric Vehicle Supply Equipments (EVSEs). The code in this repo allows for the National Laboratory of the Rockies (NLR) controls to interface with the real Distributed Energy Resource Management System (DERMS) and EVSEs in NLR's ESIF Optimization and Control Laboratory (OCL). Different charge management algorithms can be tested to determine which power allocation method is most effective with the overall goal of demonstrating clear and well documented test results as well as providing functional control algorithms which could be utilized to provide effective smart charge management (SCM) at EV charging stations. Different power allocation methods are programmed in lab_demo_controller.py and include allocation based on first come first served, equal sharing, state of charge (SOC), priority factors, and behind the meter control methods.

Panossian, Nadia [National Laboratory of the Rocki↗
NASA NTRS2020

Model-Reference Adaptive Control of Distributed Lagrangian Infinite-Dimensional Systems Using Hamilton’s Principle

This paper presents a Hamilton's principle for distributed control of infinite-dimensional systems modeled by a distributed form of the Euler-Lagrange method. The distributed systems are governed by a system of linear partial differential equations in space and time. A generalized potential energy expression is developed that can capture most physical systems including those systems that have no spatial distribution. The Hamilton's principle is applied to derive distributed feedback control methods without resorting to the standard weak-form discretization approach to convert an infinite-dimensional systems to a finite-dimensional systems. It can be shown by the principle of least action that the distributed control synthesized by the Hamilton's principle is a minimum-norm control. A model-reference adaptive control framework is developed for distributed Lagrangian systems in the presence of uncertainty. The theory is demonstrated by an application of adaptive flutter suppression control of a flexible aircraft wing.

Nguyen, Nhan T.↗
DOE OSTI2024

High-bandwidth Dynamic Load Emulation of Mechanical Systems using Electric Drives

Machine drives are versatile systems that can be programmed to emulate a variety of mechanical loads. In this paper, we walk through the modeling and control framework of a shaft-coupled dual-motor drive system that is programmed to emulate a fictitious mechanical system. We quantitatively evaluate the control performance of such a system and derive a theoretical limit that explains its inaccuracy at high operating frequencies. To overcome this problem, we propose an alternate control structure that achieves the same control objective at high frequencies as well. After suitable adjustments are made to the controller, we validate its performance using simulation results.

machine drives ,load emulation, speed-torque chara↗
DOE OSTI2021

Visual Tool for Assessing Stability of DER Configurations on Three-Phase Radial Networks

Here, we present a method and tool for evaluating the placement of Distributed Energy Resources (DER) on distribution circuits in order to control voltages and power flows. Our previous work described Phasor-Based Control (PBC), a novel control framework where DERs inject real and reactive power to track voltage magnitude and phase angle targets. Here, we employ linearized power flow equations and integral controllers to develop a linear state space model for PBC acting on a three-phase unbalanced network. We use this model to evaluate whether a given inverter-based DER configuration admits a stable set of controller gains, which cannot be done by analyzing controllability nor by using the Lyapunov equation. Instead, we sample over a parameter space to identify a stable set of controller gains. Our stability analysis requires only a line impedance model and does not entail simulating the system or solving an optimization problem. We incorporate this assessment into a publicly available visualization tool and demonstrate three processes for evaluating many control configurations on the IEEE 123-node test feeder (123NF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems

With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1992

Experiments in Nonlinear Adaptive Control of Multi-Manipulator, Free-Flying Space Robots

Sophisticated robots can greatly enhance the role of humans in space by relieving astronauts of low level, tedious assembly and maintenance chores and allowing them to concentrate on higher level tasks. Robots and astronauts can work together efficiently, as a team; but the robot must be capable of accomplishing complex operations and yet be easy to use. Multiple cooperating manipulators are essential to dexterity and can broaden greatly the types of activities the robot can achieve; adding adaptive control can ease greatly robot usage by allowing the robot to change its own controller actions, without human intervention, in response to changes in its environment. Previous work in the Aerospace Robotics Laboratory (ARL) have shown the usefulness of a space robot with cooperating manipulators. The research presented in this dissertation extends that work by adding adaptive control. To help achieve this high level of robot sophistication, this research made several advances to the field of nonlinear adaptive control of robotic systems. A nonlinear adaptive control algorithm developed originally for control of robots, but requiring joint positions as inputs, was extended here to handle the much more general case of manipulator endpoint-position commands. A new system modelling technique, called system concatenation was developed to simplify the generation of a system model for complicated systems, such as a free-flying multiple-manipulator robot system. Finally, the task-space concept was introduced wherein the operator's inputs specify only the robot's task. The robot's subsequent autonomous performance of each task still involves, of course, endpoint positions and joint configurations as subsets. The combination of these developments resulted in a new adaptive control framework that is capable of continuously providing full adaptation capability to the complex space-robot system in all modes of operation. The new adaptive control algorithm easily handles free-flying systems with multiple, interacting manipulators, and extends naturally to even larger systems. The new adaptive controller was experimentally demonstrated on an ideal testbed in the ARL-A first-ever experimental model of a multi-manipulator, free-flying space robot that is capable of capturing and manipulating free-floating objects without requiring human assistance. A graphical user interface enhanced the robot usability: it enabled an operator situated at a remote location to issue high-level task description commands to the robot, and to monitor robot activities as it then carried out each assignment autonomously.

Chen, Vincent Wei-Kang↗
NASA NTRSDate not supplied

Maximum-Likelihood Parameter Estimation for High-Contrast Wavefront Sensing & Control

Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. At present, the deepest contrast has been achieved using model-based algorithms, which use the predicted focal-plane influence of the coronagraph's deformable mirrors to drive diffracted starlight toward zero over time in an optimal control framework. However, model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the model predictions causes reduced control loop performance. Here, we report on a technique for maximum-likelihood estimation of the wavefront control Jacobian matrix and noise statistics of the coronagraph focal-plane electric field from data acquired in situ during closed-loop wavefront control operations. By empirically tuning the Jacobian and noise properties in a statistically rigorous fashion, the maximum-likelihood approach mitigates model mismatch and recovers near-optimal control loop performance.

coronagraphy↗
DOE OSTI2023

Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks

An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking, and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput; a reduction in latency of > 20%; and an increase in throughput of approximately 11% during the DoS attack scenarios.

Agnew Jr., Dennis↗
NASA NTRS2008

Modeling-Error-Driven Performance-Seeking Direct Adaptive Control

This paper presents a stable discrete-time adaptive law that targets modeling errors in a direct adaptive control framework. The update law was developed in our previous work for the adaptive disturbance rejection application. The approach is based on the philosophy that without modeling errors, the original control design has been tuned to achieve the desired performance. The adaptive control should, therefore, work towards getting this performance even in the face of modeling uncertainties/errors. In this work, the baseline controller uses dynamic inversion with proportional-integral augmentation. Dynamic inversion is carried out using the assumed system model. On-line adaptation of this control law is achieved by providing a parameterized augmentation signal to the dynamic inversion block. The parameters of this augmentation signal are updated to achieve the nominal desired error dynamics. Contrary to the typical Lyapunov-based adaptive approaches that guarantee only stability, the current approach investigates conditions for stability as well as performance. A high-fidelity F-15 model is used to illustrate the overall approach.

Kulkarni, Nilesh V.↗
DOE OSTI2022

Learning-based Anticipatory Control of Microreactors

Learning-based anticipatory control advances the level of autonomy of microreactor control systems, a key consideration for the unattended operation of fission batteries. In this work, an anticipatory control system is shown to provide accurate load-following for a microreactor by utilizing a model predictive control framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Decarbonizing all-electric communities via carbon-responsive control of behind-the-meter resources

The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Rodriguez, Guillermo↗
NASA NTRS2012

Aeroelastic Modeling of Elastically Shaped Aircraft Concept via Wing Shaping Control for Drag Reduction

Lightweight aircraft design has received a considerable attention in recent years as a means for improving cruise efficiency. Reducing aircraft weight results in lower lift requirements which directly translate into lower drag, hence reduced engine thrust requirements during cruise. The use of lightweight materials such as advanced composite materials has been adopted by airframe manufacturers in current and future aircraft. Modern lightweight materials can provide less structural rigidity while maintaining load-carrying capacity. As structural flexibility increases, aeroelastic interactions with aerodynamic forces and moments become an increasingly important consideration in aircraft design and aerodynamic performance. Furthermore, aeroelastic interactions with flight dynamics can result in issues with vehicle stability and control. Abstract This paper describes a recent aeroelastic modeling effort for an elastically shaped aircraft concept (ESAC). The aircraft model is based on the rigid-body generic transport model (GTM) originally developed at NASA Langley Research Center. The ESAC distinguishes itself from the GTM in that it is equipped with highly flexible wing structures as a weight reduction design feature. More significantly, the wings are outfitted with a novel control effector concept called variable camber continuous trailing edge (VCCTE) flap system for active control of wing aeroelastic deflections to optimize the local angle of attack of wing sections for improved aerodynamic efficiency through cruise drag reduction and lift enhancement during take-off and landing. The VCCTE flap is a multi-functional and aerodynamically efficient device capable of achieving high lift-to-drag ratios. The flap system is comprised of three chordwise segments that form the variable camber feature of the flap and multiple spanwise segments that form a piecewise continuous trailing edge. By configuring the flap camber and trailing edge shape, drag reduction could be achieved. Moreover, some parts of the flap system can be made to have a high frequency response for roll control, gust load alleviation, and aeroservoelastic (ASE) modal suppression control. Abstract The aeroelastic model of the ESAC is based on one-dimensional structural dynamic theory that captures the aeroelastic deformation of a wing structure in a combined motion that involves flapwise bending, chordwise bending, and torsion. The model includes the effect of aircraft propulsion due to wing flexibility which causes the propulsive forces and moments to couple with the wing elastic motion. Engine mass is also accounted in the model. A fuel management model is developed to describe the wing mass change due to fuel usage in the main tank and wing tanks during cruise. Abstract The model computes both static and dynamic responses of the wing structures. The static aeroelastic deflections are used to estimate the effect of wing flexibility on induced drag and the potential drag reduction by the VCCTE flap system. A flutter analysis is conducted to estimate the flutter speed boundary. Gust load alleviation via adaptive control has been recently investigated to address flexibility of aircraft structures. A multi-objective flight control approach is presented for drag reduction control. The approach is based on an optimal control framework using a multi-objective cost function. Future studies will demonstrate the potential benefits of the approach.

Aeroelasticity↗
DOE OSTI2026

Towards libraries of different ultrasmall amorphous silica cage topologies

Cage topology controlled at the nanometer length scale is expected to enable original functionalities, e.g., in catalysis and therapeutics. Cages are fundamental structural motifs of clathrates and their topological duals, Frank-Kasper phases, and are constituents of mesoporous silica frameworks. However, with one exception, they have not been synthesized as discrete particles. By varying reactant ratios of surfactant, oil, and silane, we report the discovery of 5(12)6(2) and 5(12)6(8) amorphous silica polyhedra, each coexisting with the previously identified 5(12) cage, using combined cryo-transmission electron microscopy (cryo-TEM) and single-particle reconstruction (SPR). Notably, the 5(12)6(8) polyhedron represents a topology not previously observed in mesoporous silica frameworks. Control over structural features is demonstrated, and insights into cage formation mechanisms are provided. Structural outcomes are summarized in a ternary morphology diagram alongside prior results, bridging serendipitous discovery and intentional design of silica cages displaying a level of control over silica polymerization rivaling nature.

Jang, Dong June [Department of Materials Science a↗