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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-630: Predictive Modeling of Mortality, Tumor Control, and Normal Tissue Complications After Stereotactic Body Radiotherapy for Stage I Non-Small Cell Lung Cancer

Purpose: While rates of local control have been well characterized after stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC), less data are available characterizing survival and normal tissue toxicities, and no validated models exist assessing these parameters after SBRT. We evaluate the reliability of various machine learning techniques when applied to radiation oncology datasets to create predictive models of mortality, tumor control, and normal tissue complications. Methods: A dataset of 204 consecutive patients with stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT) at the University of Pennsylvania between 2009 and 2013 was used to create predictive models of tumor control, normal tissue complications, and mortality in this IRB-approved study. Nearly 200 data fields of detailed patient- and tumor-specific information, radiotherapy dosimetric measurements, and clinical outcomes data were collected. Predictive models were created for local tumor control, 1- and 3-year overall survival, and nodal failure using 60% of the data (leaving the remainder as a test set). After applying feature selection and dimensionality reduction, nonlinear support vector classification was applied to the resulting features. Models were evaluated for accuracy and area under ROC curve on the 81-patient test set. Results: Models for common events in the dataset (such as mortality at one year) had the highest predictive power (AUC = .67, p < 0.05). For rare occurrences such as radiation pneumonitis and local failure (each occurring in less than 10% of patients), too few events were present to create reliable models. Conclusion: Although this study demonstrates the validity of predictive analytics using information extracted from patient medical records and can most reliably predict for survival after SBRT, larger sample sizes are needed to develop predictive models for normal tissue toxicities and more advanced machine learning methodologies need be consider in the future.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Data-driven building energy modeling with feature selection and active learning for data predictive control

Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this work, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Mitigation of Motor Stalling and FIDVR via Energy Storage Systems with Temporal Logic Specifications

The fault-induced delayed voltage recovery (FIDVR) phenomenon has been very common from the distribution system through the transmission system. It causes a delay on recovering significantly depressed local voltage after the fault is cleared, and it can also lead to more widespread cascading system failures. Mitigating this event with current control approaches is challenging and becoming a crucial issue. Here, a model predictive control-based strategy employing signal temporal logic specifications is proposed to help mitigate FIDVR. To this end, it investigates and extends a dynamic performance model allowing analytic insights into the system-wide impact of motor stalling and FIDVR. The proposed controller provides richer descriptions of voltage specifications addressing both magnitude and time simultaneously. We consider different control specifications with reactive power support from energy storage systems to prevent the voltage during/after the fault from dropping too low, and reduce the delay time of voltage recovery. The simulation results conducted with the IEEE 57 bus test network validate the proposed method and demonstrate the effectiveness of the mitigation strategy on FIDVR.

25 ENERGY STORAGE↗
NASA NTRS2002

Modeling and control for closed environment plant production systems

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

NASA Discipline Life Support Systems↗
DOE OSTI2026

A bi-level advanced control framework for large-scale control of buildings with system-level impact

Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal. This bi-level control is based on the previously-developed ANPV-MPC, a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time. By shifting the building electricity demand to better match the available power, the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system. The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of ±5%. Additionally, the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions, further amplifying the system-level reliability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Cooperative On-Ramp Merging with Time-Varying Vehicle-to-Vehicle Communication Delay Compensation via a Model-Free Approach

Cooperative merging strategies enabled by vehicle-to-vehicle (V2V) communication have shown promise in addressing congestion, fuel inefficiency, and collision risks. However, their performance can be severely degraded by time-varying and uncertain communication delays-an issue often overlooked in existing research, which primarily focuses on merging sequence determination and trajectory planning. Furthermore, practical considerations such as heterogeneous vehicle dynamics, varying road conditions, and real-time implementation complexities are frequently neglected. This paper presents a model-free, online planning framework for cooperative on-ramp merging of connected and automated vehicles (CAVs), explicitly accounting for time-varying V2V communication delays. Without relying on detailed vehicle dynamics, the proposed method introduces a data-driven delay compensation scheme. A co-simulation platform integrating high-fidelity vehicle dynamics, traffic simulation (SUMO), and V2V communication within MATLAB/Simulink is developed to evaluate the proposed method. Simulation results demonstrate that unaddressed V2V communication delays significantly impair merging performance. In contrast, the proposed framework enhances intervehicle distance tracking and maintains low CO2 emissions and fuel consumption, under communication delay across different communication frequencies. In conclusion, its lightweight design also facilitates real-time implementation, making it well-suited for deployment in practical CAV systems.

Accounting↗
NASA NTRS1998

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

Motter, Mark A.↗
NASA NTRS1995

Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

Ahumada, Albert J., Jr.↗
NASA NTRS1999

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a self-organizing map (SOM). Multiple self-organizing maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experiential knowledge gained from decades of operation. Each SOM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Mark A. Motter↗
DOE OSTI2025

A Sequential Model Predictive and Deep Reinforcement Learning-Based Controller for Distribution System Outage Mitigation under Hurricane Events

This paper proposes a proactive outage mitigation framework for power distribution networks to withstand hurricane-induced disruptions. It leverages Model Predictive Control (MPC) to identify safe lines for proactive switching during hurricanes, minimizing the risk of cascading failures and voltage violations. The switching strategies optimized by MPC are sequentially integrated with a Deep Reinforcement Learning agent using the Advantage Actor-Critic algorithm, enabling dynamic line switching to maximize connected buses and minimize voltage violations in real time. Using a probabilistic hurricane model, the framework predicts line failures and adapts to varying conditions to enhance grid resilience. Simulations on the IEEE 123-bus system demonstrate its effectiveness in maintaining high connectivity and minimizing disruptions. Real-time testing with an RTDS confirms the practicality and reliability of the proposed approach.

Selim, Alaa [University of Connecticut]↗
DOE OSTI2015

Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.

25 ENERGY STORAGE↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2021

Application of Artificial Neural Network Model for Optimized Control of Condenser Water Temperature Set-Point in a Chilled Water System

Here, in this study, real-time predictive control and optimization model based on an ANN (artificial neural network) was developed to evaluate the cooling energy saving performance of the optimized control of CndWT (condenser water temperature). For this purpose, the difference in TCEC (total cooling energy consumption) between the conventional control strategy when the CndWT produced by the cooling tower is fixed and the optimized control strategy when real-time control of the CndWT through the optimal ANN model is applied was compared and analyzed. For the modeling of the building to be simulated, the co-simulation of EnergyPlus and MATLAB was built through the middleware Building Controls Virtual Test Bed. For the prediction of TCEC, an ANN model was developed through MATLAB's neural network toolbox. The model accuracy of the ANN was examined through Cv(RMSE) index and as a result, Cv(RMSE) of the optimized ANN model turned out to be approximately 25 %. More importantly, the predictive control technique was able to save TCEC by 5.6 % compared to the conventional control method constantly fixing CndWT set-point to 30 °C. These results showed that the CndWT needs to be dynamically controlled using artificial intelligence technique such as ANN model and that significant energy savings were achievable compared to the conventional fixed control.

42 ENGINEERING↗
NASA NTRS1986

The effects of voice and manual control mode on dual task performance

Two fundamental principles of human performance, compatibility and resource competition, are combined with two structural dichotomies in the human information processing system, manual versus voice output, and left versus right cerebral hemisphere, in order to predict the optimum combination of voice and manual control with either hand, for time-sharing performance of a dicrete and continuous task. Eight right handed male subjected performed a discrete first-order tracking task, time-shared with an auditorily presented Sternberg Memory Search Task. Each task could be controlled by voice, or by the left or right hand, in all possible combinations except for a dual voice mode. When performance was analyzed in terms of a dual-task decrement from single task control conditions, the following variables influenced time-sharing efficiency in diminishing order of magnitude, (1) the modality of control, (discrete manual control of tracking was superior to discrete voice control of tracking and the converse was true with the memory search task), (2) response competition, (performance was degraded when both tasks were responded manually), (3) hemispheric competition, (performance degraded whenever two tasks were controlled by the left hemisphere) (i.e., voice or right handed control). The results confirm the value of predictive models invoice control implementation.

Wickens, C. D.↗