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Results for “Model Predictive Control”

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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Ocean-Science Mission Needs: Real-Time AUV Data for Command, Control, and Model Inputs

Predictive models for tides, hydrodynamics, and bio-optical properties affecting the visibility and buoyancy of coastal waters are needed to evaluate the safety of personnel and equipment engaged in maritime operations under potentially hazardous conditions. Predicted currents can be markedly different for two-layer systems affected by terrestrial runoff than for well-mixed conditions because the layering decouples the surface and bottom Ekman layers and rectifies the current response to oscillatory upwelling-and downwelling-favorable winds. Standard ocean models (e.g. Princeton Ocean Model) require initial-and boundary data on the physical and optical properties of the multilayered water column to provide accurate simulations of heat budgets and circulation. Two observational systems are designed to measure vertically structured conditions on the West Florida Shelf (WFS): a tethered buoy network and an autonomous underwater vehicle (AUV) observational system. The AUV system is described with a focus on the observational systems that challenge or limit the communications command and control network for various types of measurement programs. These include vertical oscillatory missions on shelf transects to observe the optical and hydrographic properties of the water column, and bottom-following missions for measuring the bottom albedo. Models of light propagation, absorption, and conversion to heat as well as determination of the buoyancy terms for physical models require these measurements. High data rates associated with video bottom imagery are the most challenging for the real-time, command and control communications system, but they are met through a combination of loss-less and lossy data-compression methods, depending upon the data-rate of the radio links.

Carder, Kendall L.↗
DOE OSTI2017

Overview of simulation and control tools for extended operability of nuclear reactors - 183

The increasing penetration of Renewable Energy Sources (RES) requires the current power grid to be suitably improved. Because of the unsteady nature of RES, as long as energy storage facilities are not deployed and commercialized, an increased effort for the frequency regulation in order to ensure high reliability and performance is necessary. In such a scenario, nuclear units (traditionally deployed as baseload generating units) will be required to cooperate with intermittent RES-based power plants. Therefore, new reactors will need larger operational flexibility with respect to the capabilities of the currently operated units. Over the last few years, a comprehensive research program oriented at developing a suite of simulation tools and control system development capabilities was pursued at Argonne National Laboratory. In addition to the study of advanced reactor concepts (e.g. metal-fueled Sodium-cooled Fast Reactors) and technological innovations (such as S-CO{sub 2} Energy Conversion Cycles), new control approaches were developed to improve the operability of nuclear power plants. In particular, Model-based Predictive Control algorithm for Multiple Input Multiple Output control strategies and set-point definition algorithm based on Reference Governor were studied and applied to specific scenarios, providing with a range of novel capabilities able to meet the expected greater sets of requirements mentioned above. In this work, an overview of such tools and case studies is presented with particular focus on their impact on operability and economic competitiveness of nuclear energy. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

Piloted Simulation Evaluation of a Model-Predictive Automatic Recovery System to Prevent Vehicle Loss of Control on Approach

This paper describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

aircraft engine simulation↗
NASA NTRS2014

Piloted Simulation Evaluation of a Model-Predictive Automatic Recovery System to Prevent Vehicle Loss of Control on Approach

This paper describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

aircraft engine performance↗
DOE OSTI2013

Final Report - DE-EE0000260

In this program, we are developing next-generation SOFC materials and architectures for use with biomass-derived fuel streams, with a focus on addressing the technical challenges and the required operating windows associated with carbon-deposit formation. We are developing fuel-processing strategies for processing landfill- and anaerobic-digester-derived biomass streams for use in SOFCs. Similarly, we are examining processing of biomass-derived liquid fuels for use in solid-oxide fuel cell systems. We conduct extensive computational modeling work to support the materials-development and fuel-processing tasks. Modeling is conducted across a broad range of length scales, from the microscopic level to examine the fundamental underpinnings of cell operation, to the mesoscopic level to examine and optimize fuel-reformer design, and to the system level to craft optimal integration and dynamic-control solutions.

03 NATURAL GAS↗
DOE OSTI2023

Machine learning based simultaneous control of air handling unit discharge air and condenser water temperatures set-point for minimized cooling energy in an office building

In this study, an artificial intelligence based real-time prediction and control model to optimize condenser water temperature and discharge air temperature (DAT) set-points in water-cooled air handling unit (AHU) system has been developed. EnergyPlus-MATLAB co-simulation has been conducted to analyze the developed model's effectiveness. Here, to develop artificial neural networks (ANN) model, embedded neural network objects in MATLAB was utilized. The developed model could decide an optimal temperature set-points based on outdoor air wet-bulb temperature to reflect the Korean climate context. As a result, the developed ANN prediction model showed the predictive performance of Cv(RMSE) of approximately 21%. Compared to the conventional fixed temperature algorithm, which fixes AHU DAT at 14°C and condenser water temperature at 32°C, the ANN based optimized control showed a 22% total cooling energy reduction. These results show that significant energy savings can be achieved by simultaneously controlling condenser water temperature and AHU DAT set-points considering Korean climatic characteristics using AI technologies such as ANN models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Application of periodic perturbations to a RELAP5 hydraulic simulation to determine frequency response

Time series analysis requires a collection of data taken sequentially in time to apply techniques for identification, modeling, prediction or control purposes. Some systems operating at steady state are perturbed with white noise to extract system information using autoregressive models or spectral analysis, and then the dynamic response is used to assess system properties. If it is not possible to perturb the physical system, a mathematical model can be used to work with and study the perturbation effect. In this work, the system object of perturbation study is a RELAP5 hydraulic model, and since the excitation with a white noise sequence is not readily implementable, an alternative is proposed to apply a series of periodic steps in a square wave pattern. With this periodic excitation, the output of a RELAP5 model for a hydraulic piping system can be analyzed with frequency domain tools. The piping systems can be found in the balance of plant of a steam power plant, and the aim of this work is to explore the capability of RELAP5 to determine system frequency characteristics linked to mechanical vibration in operating lines of a power plant. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2011

Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

Miller, Christopher J.↗
DOE OSTI2020

Duty-Cycle Predictive Control of Quasi-Z-Source Modular Cascaded Converter Based Photovoltaic Power System

A duty-cycle predictive control is proposed for dc grid integration of front-end isolated quasi-Z-source modular cascaded converter (qZS-MCC) photovoltaic (PV) power system. The post-stage qZS half-bridge dc-dc converter deals with PV maximum power point tracking, dc grid integration, and dc-link voltage balance; whereas, the front-end isolation converters operate at a constant duty cycle of 50%. Thus, it saves control hardware resources while overcoming challenges from PV-panel voltage variations and dc-bus voltage limit. The proposed control uses the derived circuit model to predict the global active-state duty cycle for grid-connected current control and predict the shoot-through duty cycles for dc-link voltage balance, achieving a fast and accurate tracking target. The proposed control method has advantages of: i) eliminating weighting factors that exist in conventional model predictive control (MPC), ii) no sophisticated loop parameters design that exists in proportional-integral (PI) control, iii) operating at constant switching frequency that is different from the conventional MPC with variable switching frequency. Simulation and experimental tests are carried out to verify the effectiveness of the proposed control method and compare with the PI-based control system.

42 ENGINEERING↗
DOE OSTI2024

Elastic Changepoint Detection for Globally-indexed Functional Time Series Data with Climate Applications

Changepoint detection is a vital tool in the application of climate data analysis. Numerous types of climate observation data are most properly represented by functional time series, implying a need for accurate changepoint detection methods applicable to functional time series data. Such data taken at a global scale often contain both spatial heterogeneity and dependence as well as phase (time) misalignment. In this report, we present methods which can detect spatially-dependent changepoints while allowing different estimates of change time and change strength depending on location. Additionally, we provide extensions to this spatially-predicted model which controls for phase variability among observations. Our methods provide the ability to detect a single change, or control for epidemic changes (where a “return-to-normal” change is more likely to be detected than the initial change). We showcase results analyzing the June 1991 eruption of Mt. Pinatubo, where our methods demonstrate the ability to accurately detect both single and epidemic changepoints even in the presence of strong seasonal variability. We find that our spatially-predicted model improves the detection of relevant changepoints versus methods which do not take spatial information into account, and we find that controlling for phase variability helps to control the false discovery rate during the detection process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks↗
DOE OSTI2023

Assessing the Impact of Cybersecurity Attacks on Energy Systems

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks.

buildings↗
DOE OSTI2023

Review of rotating wing dynamic stall: Experiments and flow control

Dynamic stall has been a technical challenge and a fluid dynamical subject of interest for more than fifty years; but in the last decade significant advances have been made in the understanding, prediction, modeling, and control of dynamic stall on rotors. This paper provides a summary of the state of the art of dynamic stall experiments and future directions in the understanding of dynamic stall on rotors. Experimental data sets are discussed, as well the direction of future research for control of dynamic stall. Coordinated testing between airfoils and rotating blades, as well as close integration between computational and experimental studies were found to be productive approaches. Advanced analysis methods, including statistical methods, modal representations, and artificial intelligence methods have led to significant advances in the understanding of dynamic stall. Investigations of dynamic stall control devices have allowed many useful targeted investigations of the transition to separated flow, but have not yet resulted in a commercially implemented device.

42 ENGINEERING↗
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗
DOE OSTI2011

Supervisory control of a pilot-scale cooling loop

We combine a previously developed strategy for Fault Detection and Identification (FDI) with a supervisory controller in closed loop. The combined method is applied to a model of a pilot-scale cooling loop of a nuclear plant, which includes Kalman filters and a model-based predictive controller as part of normal operation. The system has two valves available for flow control meaning that some redundancy is available. The FDI method is based on likelihood ratios for different fault scenarios which in turn are derived from the application of the Kalman filter. A previously introduced extension of the FDI method is used here to enable detection and identification of non-linear faults like stuck valve problems and proper accounting of the time of fault introduction. The supervisory control system is designed so to take different kinds of actions depending on the status of the fault diagnosis task and on the type of identified fault once diagnosis is complete. Some faults, like sensor bias and drift, are parametric in nature and can be adjusted without need for reconfiguration of the regulatory control system. Other faults, like a stuck valve problem, require reconfiguration of the regulatory control system. The whole strategy is demonstrated for several scenarios.

47 OTHER INSTRUMENTATION↗