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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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Paper targets SPEEDAM 2024 (https://www.speedam.org). The paper provides details of a model predictive control designed to operate a four-zone medium-voltage AC/DC electric ship.
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.
Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.
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.
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.
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.
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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.
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.
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.
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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.
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This paper describes a modeling method and a new optimal control approach to investigate a Mach number control problem for the NASA Ames 11-Foot Transonic Wind Tunnel. The flow in the wind tunnel is modeled by the 1-D unsteady Euler equations whose boundary conditions prescribe a controlling action by a compressor. The boundary control inputs to the compressor are in turn controlled by a drive motor system and an inlet guide vane system whose dynamics are modeled by ordinary differential equations. The resulting Euler equations are thus coupled to the ordinary differential equations via the boundary conditions. Optimality conditions are established by an adjoint method and are used to develop a model predictive linear-quadratic optimal control for regulating the Mach number due to a test model disturbance during a continuous pitch
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An analytical model was developed to predict the noise levels inside propeller-driven aircraft during cruise at M = 0.8. The model was applied to three study aircraft with fuselages of different size (wide body, narrow body and small diameter) in order to determine the noise reductions required to achieve the goal of an A-weighted sound level which does not exceed 80 dB. The model was then used to determine noise control methods which could achieve the required noise reductions. Two classes of noise control treatments were investigated: add-on treatments which can be added to existing structures, and advanced concepts which would require changes to the fuselage primary structure. Only one treatment, a double wall with limp panel, provided the required noise reductions. Weight penalties associated with the treatment were estimated for the three study aircraft.
Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.