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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

model predictive control↗
NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

model predictive control↗
NASA NTRSDate not supplied

Turbine Electrified Energy Management with Model Predictive Control

Affordability, sustainability, and efficiency are primary motivators driving the future of NASA aeronautics research. These factors are realized, in part, through the development and implementation of new technologies and strategies enabling efficient, affordable, and safe hybrid-electric aircraft. Research supporting electrified aircraft propulsion control systems exemplifies such new methodologies, offering varied opportunities to integrate electric machines with gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures seek to introduce energy storage and exploit electrical power system components to assist gas-based system components. Capitalizing on the electric machines in hybridized engines, Turbine Electrified Energy Management (TEEM) is a control approach that enhances transient operability to improve overall propulsion and vehicle efficiency by injecting or extracting power from engine shafts. Traditionally implemented with proportional-integral (PI) control, this study expands the application of TEEM by presenting model predictive control (MPC) schemes to execute the TEEM concept. Via cost function design and constraint selection, the transient operability goals for TEEM are considered in the controller designs. The proposed MPCs are simulated on a nonlinear turbofan engine model at two environmental conditions, with comparisons drawn to a baseline PI. Performance is evaluated using compressor maps and two TEEM-specific metrics: transient stack usage and transient excursion integral. Simulation results reveal the developed schemes perform comparably to the benchmark controller and can be implemented in two distinct configurations. Potential modifications for future investigations include cost function measures that optimize energy use, additional performance effectiveness measures, and battery storage capabilities.

Elyse D. Hill↗
NASA NTRS2019

Partition-based Feasible Integer Solution Pre-computation for Hybrid Model Predictive Control

For multiparametric mixed-integer convex programming problems such as those encountered in hybrid model predictive control, we propose an algorithm for generating a feasible partition of a subset of the parameter space. The result is a static map from the current parameter to a suboptimal integer solution such that the remaining convex program is feasible. Convergence is proved with a new insight that the overlap among the feasible parameter sets of each integer solution governs the partition complexity. The partition is stored as a tree which makes querying the feasible solution efficient. The algorithm can be used to warm start a mixed integer solver with a real-time guarantee or to provide a reference integer solution in several suboptimal MPC schemes. The algorithm is tested on randomly generated systems with up to six states, demonstrating the effectiveness of the approach.

Bayard, David S.↗
NASA NTRS1983

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Cull, R. C.↗
NASA NTRS2017

Stall Recovery Guidance Using Fast Model Predictive Control

Based on a detailed analysis of recent loss-of-control events, the Aircraft State Awareness Joint Safety Analysis Team has identified the need to develop algorithms and display strategies to provide control guidance for recovery from approach-to-stall or stall. In order to be effective, such guidance should enhance the pilots ability to execute the Federal Aviation Administrations recommended stall recovery procedure. This paper explores the use of a fast model predictive control algorithm that determines near optimal recovery guidance, which quantifies the aircraft configuration and situation dependent recovery information required to maximize the effectiveness of the recovery. This information includes the magnitude of the initial pitch down maneuver, the specific amount of airspeed and thrust needed before pulling out of the recovery dive, as well as the maximum pitch-up rate that can be sustained without causing a secondary stall. The algorithm was integrated and tested with an in-house desktop simulator that implements the General Transport Aircraft model and the associated stall aircraft dynamics. Preliminary results are presented to demonstrate the use of the proposed approach as a recovery aid for pilots.

predictive control↗
NASA NTRS2006

Exploratory Studies in Generalized Predictive Control for Active Gust Load Alleviation

The results of numerical simulations aimed at assessing the efficacy of Generalized Predictive Control (GPC) for active gust load alleviation using trailing- and leading-edge control surfaces are presented. The equations underlying the method are presented and discussed, including system identification, calculation of control law matrices, and calculation of commands applied to the control effectors. Both embedded and explicit feedforward paths for inclusion of disturbance effects are addressed. Results from two types of simulations are shown. The first used a 3-DOF math model of a mass-spring-dashpot system subject to user-defined external disturbances. The second used open-loop data from a wind-tunnel test in which a wing model was excited by sinusoidal vertical gusts; closed-loop behavior was simulated in post-test calculations. Results obtained from these simulations have been decidedly positive. In particular, results of closed-loop simulations for the wing model showed reductions in root moments by factors as high as 1000, depending on whether the excitation is from a constant- or variable-frequency gust and on the direction of the response.

Kvaternik, Raymond G.↗
NASA NTRS2001

Experimental Investigations of Generalized Predictive Control for Tiltrotor Stability Augmentation

A team of researchers from the Army Research Laboratory, NASA Langley Research Center (LaRC), and Bell Helicopter-Textron, Inc. have completed hover-cell and wind-tunnel testing of a 1/5-size aeroelastically-scaled tiltrotor model using a new active control system for stability augmentation. The active system is based on a generalized predictive control (GPC) algorithm originally developed at NASA LaRC in 1997 for un-known disturbance rejection. Results of these investigations show that GPC combined with an active swashplate can significantly augment the damping and stability of tiltrotors in both hover and high-speed flight.

Nixon, Mark W.↗
NASA NTRS2022

Model Predictive Control in the Three-body Problem Using Invariant Funnels As Terminal Sets

This paper describes a method for augmenting Model Predictive Control techniques using invariant funnels computed in the Circular Restricted Three-Body Problem. We use ellipsoids that roughly approximate the boundary of the invariant funnel as convex terminal sets for a short look-ahead optimization problem at each time step. We apply this method to a hypothetical low-thrust mission to land on Jupiter’s mooon Europa and show that including the invariant funnels as terminal sets reduces the amount of control effort required by almost an order of magnitude.

Close, Sigrid↗
NASA NTRS1997

Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

Richardson, Albert O.↗
NASA NTRS1997

Broadband Noise Control Using Predictive Techniques

Predictive controllers have found applications in a wide range of industrial processes. Two types of such controllers are generalized predictive control and deadbeat control. Recently, deadbeat control has been augmented to include an extended horizon. This modification, named deadbeat predictive control, retains the advantage of guaranteed stability and offers a novel way of control weighting. This paper presents an application of both predictive control techniques to vibration suppression of plate modes. Several system identification routines are presented. Both algorithms are outlined and shown to be useful in the suppression of plate vibrations. Experimental results are given and the algorithms are shown to be applicable to non- minimal phase systems.

Eure, Kenneth W.↗
NASA NTRSDate not supplied

A Model Predictive Control Approach for In-Flight Acoustic Constraint Compliance

Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral (MPPI) control law is used to generate a finite horizon trajectory that satisfies acoustic level constraints at a set of (three-dimensional) observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The model predictive control architecture is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.

acoustically-aware vehicle↗
NASA NTRSDate not supplied

A Model Predictive Control Approach for In-Flight Acoustic Constraint Compliance

Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility-class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral (MPPI) control law is used to generate a finite-horizon trajectory that satisfies acoustic level constraints at a set of (three-dimensional) observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The model predictive control architecture is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.

acoustically-aware vehicle↗
NASA NTRS2018

Survey of Methods to Predict Controller Workload for Real-Time Monitoring of Airspace Safety

The Real Time Safety Monitoring (RTSM) approach allows for assessment and prediction of the safety margin in the National Airspace System (NAS) to help preempt incidents and accidents, rather than having to reactively mitigate them. In RTSM, the NAS is modeled using state variables, and safety metrics are defined in terms of these state variables. The safety metrics have been classified as weather-related, airspace-related, and human-related. Many of the formulated human-related safety metrics need an estimate of the controller workload for their computation. However, this computation is not trivial. Hence, in this report, we perform a literature survey to identify the different factors that enable the computation of controller workload and categorize these factors. Next, we describe studies undertaken to determine a minimal set of factors that pro- vide a correct assessment of controller workload. Lastly, we survey approaches for evaluating how well the selected factors correlate with the controllers' subjective assessment of their workload. Based on this survey, we present factors beneficial to computing and predicting controller workload in real time, and discuss the status of data sources necessary for these computations.

National Airspace System↗
NASA NTRSDate not supplied

NASA Langley FUN3D Analyses in Support of the 1st AIAA Stability and Control Prediction Workshop

This work summarizes the results of FUN3D analyses conducted for the 1st AIAA Stability and Control Workshop on behalf of participants from the NASA Langley Research Center. The workshop was created to establish best practices for the prediction of stability and control derivatives using computational fluid dynamics and assess the limitations of these methods when those best practices are applied. The inaugural workshop considered the ONERA version of the NASA/Boeing Common Research Model, which includes the wing, body, horizontal tail, and a vertical tail designed by ONERA. Wind tunnel data at small sideslip angles remain unpublished and served as ‘blind’ data for computational comparisons. The present research generated workshop test case data using the NASA FUN3D code, which is a parallelized, unstructured, node-based, finite-volume discretization, Reynolds-averaged Navier-Stokes flow solver. Steady- state numerical simulations were conducted for workshop test cases investigating the following: grid convergence, Mach number effect on static stability, wind tunnel sting increments, static stability-derivative calculations, and a sideslip angle sweep. Results were generated for two series of unstructured, mixed-element grids, one set provided by the workshop and another set created using the HeldenMesh grid generation software. The results provided include total- and component-level breakdowns of the force and moment coefficients, in addition to sectional pressure distributions for the wing and tail components for comparisons to wind tunnel data.

CFD↗
NASA NTRSDate not supplied

A Model Predictive Control Approach for In-Flight Acoustic Constraint Compliance

Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility-class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral~(MPPI) control law is used to generate a finite-horizon trajectory that satisfies acoustic level constraints at a set of observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The proposed control law is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.

Aircraft motion planning↗
NASA NTRS2007

Flow Control Predictions using URANS Modeling: A Parametric Study

A computational study was performed for steady and oscillatory flow control over a hump model with flow separation to assess how well the steady and unsteady Reynolds-averaged Navier-Stokes equations predict trends due to Reynolds number, control magnitude, and control frequency. As demonstrated previously, the hump model case is useful because it clearly demonstrates a failing in all known turbulence models: they under-predict the turbulent shear stress in the separated region and consequently reattachment occurs too far downstream. In spite of this known failing, three different turbulence models were employed to determine if trends can be captured even though absolute levels are not. The three turbulence models behaved similarly. Overall they showed very similar trends as experiment for steady suction, but only agreed qualitatively with some of the trends for oscillatory control.

Rumsey, Christopher L.↗