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Results for “Optimal 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 1,765 records · Page 98

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NASA NTRS1992

Nonlinear feedback control of highly manoeuvrable aircraft

This paper describes the application of nonlinear quadratic regulator (NLQR) theory to the design of control laws for a typical high-performance aircraft. The NLQR controller design is performed using truncated solutions of the Hamilton-Jacobi-Bellman equation of optimal control theory. The performance of the NLQR controller is compared with the performance of a conventional P + I gain scheduled controller designed by applying standard frequency response techniques to the equations of motion of the aircraft linearized at various angles of attack. Both techniques result in control laws which are very similar in structure to one another and which yield similar performance. The results of applying both control laws to a high-g vertical turn are illustrated by nonlinear simulation.

Garrard, William L.↗

NASA NTRS1995

Analytical investigations in aircraft and spacecraft trajectory optimization and optimal guidance

A collection of analytical studies is presented related to unconstrained and constrained aircraft (a/c) energy-state modeling and to spacecraft (s/c) motion under continuous thrust. With regard to a/c unconstrained energy-state modeling, the physical origin of the singular perturbation parameter that accounts for the observed 2-time-scale behavior of a/c during energy climbs is identified and explained. With regard to the constrained energy-state modeling, optimal control problems are studied involving active state-variable inequality constraints. Departing from the practical deficiencies of the control programs for such problems that result from the traditional formulations, a complete reformulation is proposed for these problems which, in contrast to the old formulation, will presumably lead to practically useful controllers that can track an inequality constraint boundary asymptotically, and even in the presence of 2-sided perturbations about it. Finally, with regard to s/c motion under continuous thrust, a thrust program is proposed for which the equations of 2-dimensional motion of a space vehicle in orbit, viewed as a point mass, afford an exact analytic solution. The thrust program arises under the assumption of tangential thrust from the costate system corresponding to minimum-fuel, power-limited, coplanar transfers between two arbitrary conics. The thrust program can be used not only with power-limited propulsion systems, but also with any propulsion system capable of generating continuous thrust of controllable magnitude, and, for propulsion types and classes of transfers for which it is sufficiently optimal the results of this report suggest a method of maneuvering during planetocentric or heliocentric orbital operations, requiring a minimum amount of computation; thus uniquely suitable for real-time feedback guidance implementations.

Markopoulos, Nikos↗

DOE OSTI2019

Safer Batteries via Active Fault Tolerant Control

Safety remains a critical technological barrier for Lithium-ion batteries. Besides safer battery materials, intelligent battery management can be the key to safer batteries. Motivated by this scenario, we propose an active fault tolerant control scheme which can potentially improve battery safety. Essentially, we focus on battery internal thermal faults which may lead to thermal runaway, and design a control scheme to alleviate such fault effect at an early stage. The control scheme consists of a fault tolerant control algorithm coupled with a fault and state co-estimator. A multi-objective optimal control technique has been used to design the control algorithm whereas an unknown state-input co-estimation approach is used for the fault and state estimator. Finally, simulation studies illustrate the effectiveness of the proposed control scheme.

25 ENERGY STORAGE↗

NASA NTRS1986

Synthesis of control laws for optimally designed large space structures

In this study, the vibration control of large space structures using the linear quadratic regulator technique is investigated. Emphasis is made on the control of both optimally designed structures and also the original (uniform) structures using the cantilever beam as an example. The open loop and closed loop eigenvalues are compared and the transient responses are obtained to determine the effectiveness of the control system design.

Satyanarayana, K.↗

DOE OSTI2018

A data driven control strategy for optimal and flexible operation of absorption systems; Stratégie de régulation fondée sur les données pour un fonctionnement optimal et flexible des systèmes à absorption

A novel control strategy for vapor absorption systems with experimental validation is presented. Statistical methods are used to integrate a detailed thermodynamic model and system-specific characteristics into a model based control algorithm. This allows for continuously optimized operation as well as fast response to variations in operating conditions. Autonomous controller performance was tested experimentally for response to changes in heat sink and evaporator temperatures. Continuous turndown with fast response to capacity demand changes, in contrast to suboptimal on/off control, was demonstrated. The thermal compressor concept was employed to develop the variable speed thermal compressor module, with aspects specific to absorption heat pumps, also facilitating cross-applicability of vapor compression system control strategies. Our results of this study enable further development and optimization of absorption systems for a wide range of applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1970

Control of a class of nonlinear distributed parameter systems via direct search on the performance index

Direct search of performance index for optimal control of class of nonlinear distributed parameter systems

Fan, L. T.↗

NASA NTRSDate not supplied

Modified Cascading Generalized Inverse Control Allocation

The current aviation revolution towards electric propulsion aircraft (e.g., electric vertical takeoff-and-landing) brings unique control challenges. These vehicles are typically over-actuated (more effectors than desired control outcomes), may require control strategies for the three phases of flight (hover, transition and cruise), and currently have limited electric power availability. These vehicle challenges bring the need for optimal control allocation to the forefront of research. A leading control allocation algorithm, used in current flight vehicles, is the Cascading Generalized Inverse (CGI). Unfortunately, the Cascading Generalized Inverse algorithm is unable to achieve some desired outcomes, it intermittently provides non-optimal allocations, and it may fail to preserve moment direction near maximal achievable outcomes. In this research, the shortcomings of the Cascading Generalized Inverse algorithm are addressed by augmenting the algorithm with Scalar Difference Quadratic unsaturation identification and location at each iteration. Rigorous theory is shown that the Modified Cascading Generalized Inverse performs better at obtaining optimal allocations for all attainable outcomes. Numerical case studies for over-actuated vehicles demonstrate resolution to the aforementioned deficiencies.

Control Allocation↗

NASA NTRSDate not supplied

Modified Cascading Generalized Inverse Control Allocation

Control Allocation↗

DOE OSTI2018

Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗

DOE OSTI2019

Abradable Sealing Materials for Emerging IGCC-Based Turbine Systems (Final Report)

Reducing the gap between rotating and stationary parts in gas turbine engines, and mitigating gas leakage via these paths, can significantly increase the performance and attendant efficiency. One approach to reducing such gaps is to deploy abradable coatings on the stationary shroud components as seals, to reduce the blade-tip clearances under operational conditions. Abradable coatings must be able to withstand high temperature oxidation, thermal cycling, and erosion, while providing optimal controlled abrasion and associated shape retention. Existing abradable seals utilized in power generation turbines have generally been optimized for use with natural gas-fired systems; however, preliminary testing of syngas and high-hydrogen-content (HHC) fired turbines has shown that the stability of hot-section materials (and presumably abradable sealing materials) may be substantially altered due to characteristic changes in the combustion by-products (partial pressures of water vapor, etc.) as well as characteristic impurities and particulate matter entrained in the fuel. The primary focus of this research program has been to develop a mechanistic understanding of how the altered combustion environments modify the thermo-mechanical stability and performance of the sealing materials, including the requisite abradable sealing behavior. A key component of the research program carried out has been an exploration of mechanisms underpinning observed degradation processes, and an assessment of connections to the combustion environments and characteristic non-combustible constituents. The ultimate goal of the project has been to advance the goals of the Advanced Turbine Program by developing materials design protocols leading to turbine hot-section components with improved resistance to service lifetime degradation under advanced fuels exposures. This research program has evaluated the performance and degradation of ceramic abradable seals used in the high-temperature turbine sections of gas turbine engines. The focus has been two-fold: (1) to understand performance of such materials in relation to the turbines operating on coal-derived syngas and high hydrogen content (HHC) fuels, ultimately seeking an improved understanding of factors that control performance and durability of abradable seal materials for these combustion environments (and play into materials design protocols); and (2) to evaluate the potential of alternative materials as abradable seals for the higher temperatures expected with IGCC and HHC fueled turbine systems. In particular, we carried out an initial assessment of multi-layered systems of existing abradable seal materials (YSZ) and other ceramics. The research program also investigated several classes of abradable coatings under simulated exposures to syngas-based combustion environments, evaluating the relevant wear behavior, hardness, stability under cyclic oxidation, and general thermo-mechanical behavior. The research also attempted to develop alternatives to carrying out full-scale combustion-based abradability test rig exposures to evaluate materials system performance. The research has been focused on correlating the measured thermo-mechanical behavior, and controlled abrasive wear, with the intrinsic properties of the multilayer coatings and processing-controlled microstructural features that have shown attractive machinability characteristics based on weak interfacial bonding between the constituent phases at elevate temperatures (while maintaining thermochemical stability). This program has resulted in an improved mechanistic understanding of factors governing performance of high temperature abradable seals, and degradation mechanisms unique to IGCC-based power generation turbine systems and the realization of coal-derived syngas and HHC-based combustion environments – ultimately with the goal of developing a knowledge base upon which the design of coatings that retain optimal sealing characteristics and are more resistant to the observed wear/attack mechanisms, important aspects of advancing the goals of the DOE Advanced Turbine Program.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Resilient Operation of Networked Community Microgrids with High Solar Penetration

This project, funded by the US Department of Energy’s Solar Energy Technologies Office (SETO), focused on the operation of microgrids as a coordinated network. The primary objective, which was successfully achieved, was to develop both control strategies and hardware solutions to support the resilient and efficient operation of networked microgrids with high solar penetration. The work was structured around the following four main tasks: • Development of distributed and scalable optimization algorithms for AC-coupled networked microgrids. • Design and implementation of a novel DC interconnection hardware to enable precise power exchange between microgrids. • Laboratory operational validation of the developed technologies using 480 V testbeds and commercially available hardware. • Field operational validation of the complete solution in Adjuntas, Puerto Rico, interconnecting two kW-scale, split-phase microgrids of Casa Pueblo’s microgrids. This project addressed multiple technical challenges across the domains of optimization, control, hardware interconnection, and protection. One of its key contributions was delivering tangible, real-world solutions for networking microgrids. In contrast to purely theoretical or simulation-based work, this project included full-scale hardware operational validation both in the lab and in the field. The work conducted as part of this project—in collaboration with the University of Puerto Rico; the University of Tennessee, Knoxville; the University of Central Florida; and Casa Pueblo—has advanced the state of the art in networked microgrids. Key contributions include the development of distributed control strategies, practical solutions for real-world implementation challenges, and the introduction of a novel DC interlink approach for microgrid interconnection. The project featured both laboratory and field validation using commercial off-the-shelf components. The field deployment successfully validated that a group of microgrids can operate in a coordinated manner, enabling precise power flow between systems and mutual support during extreme events. This project resulted in 15 journal publications and 15 conference papers; 5 graduate students and 15 undergraduate students were supported. The codes of distributed optimization and forecasting were made open-source through OSTI.gov for distributed optimization and forecasting. All the publications are available in the ORNL-hosted project landing page. The DC interlink with state-of-charge balancing control was operationally validated in Adjuntas by interconnecting two real-world, 240 V split-phase microgrids. To the best knowledge of the team, this represents the first operational validation of AC microgrids interconnected via DC-interlinks. As a culmination of this project, a follow-on grant was awarded to support the technology transfer of the distributed optimization framework to a commercial microgrid controller, Stellar Edge, developed by the California-based company New Sun Road.

14 SOLAR ENERGY↗

DOE OSTI2017

Model Predictive Control of parametric excited pitch-surge modes in wave energy converters

For a heave-pitch-surge three-degrees-of-freedom wave energy converter, the heave mode is usually decoupled from the pitch-surge modes for small motions. The pitch-surge modes are usually coupled and are parametrically excited by the heave mode, depending on the buoy geometry. In this paper, a Model Predictive Control is applied to the parametric excited pitch-surge motion, while the heave motion is optimized independently. The optimality conditions are derived, and a gradient-based numerical optimization algorithm is used to search for the optimal control. Numerical tests are conducted for regular and Bretschneider waves. The results demonstrate that the proposed control can be implemented to harvest more than three times the energy that can be harvested using a heave-only wave energy converter. As a result, the energy harvested using a parametrically excited model is higher than that is harvested when using a linear model.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Metric Learning to Accelerate Convergence of Operator Splitting Methods

Recent developments in machine learning have led to promising advances in accelerating the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of proposed strategies for learning to produce fast solutions to optimization problems. For example, recent works have shown that it is possible to accelerate the convergence of optimization algorithms by learning to select their parameters, such as gradient descent stepsizes. This work proposes a new approach, in which the underlying metric spaces of proximal operator splitting algorithms are learned to maximize convergence rate. While prior works in optimization theory have derived optimal metrics in simple cases, no such result exists for many practical problem forms including general Quadratic Programming (QP). This paper shows how differentiable optimization can enable the end-to-end learning of proximal metrics, enhancing the convergence of proximal algorithms for QP problems beyond what is possible based on known theory. Additionally, the results illustrate a strong connection between the learned proximal metrics and active constraints at the optima, leading to an interpretation in which the predicted proximal metrics can be viewed as a form of active set prediction.

King, Ethan [BATTELLE (PACIFIC NW LAB)]↗

NASA NTRS1971

Quadratic performance index generation for optimal regular design.

Application of optimal control theory to practical problems has been limited by the difficulty of prescribing a performance index which accurately reflects design requirements. The task of deriving equivalent performance indices is considered in the present paper for a plant that is a completely controllable, scalar linear system with state feedback. A quadratic index is developed which leads to an optimal design performance satisfying some of the classical performance criteria.

Bullock, T. E.↗

NASA NTRS1979

Application of two synthesis methods for active flutter suppression on an aeroelastic wind tunnel model

Two flutter suppression control laws have been synthesized, implemented, and demonstrated on an aeroelastic wind-tunnel model of a transport-type wing. One control law was synthesized using an aerodynamic energy method and the other from using results of optimal control theory. At M = 0.95, the model was tested to a dynamic pressure 44 percent above the system-off flutter dynamic pressure. Both synthesis methods yielded control laws effective in suppressing flutter. The experimental results also indicate that wind-tunnel turbulence is an important factor in the experimental demonstration of system performance.

Abel, I.↗

NASA NTRS1990

Real time control for NASA robotic gripper

The ability to easily manipulate objects in a zero gravity environment will pay a key role in future space activities. Emphasis will be placed on robotic manipulation. This will serve to increase astronaut safety and utility in addition to several other benefits. The aim is to develop control laws for the zero gravity robotic end effectors. A hybrid force/position controller will be used. Sensory data available to the controller are obtained from an array of strain gauges and a linear potentiometer. Applying well known optimal control theoretical principles, the control which minimizes the transition time between positions is obtained. A robust force control scheme is developed which allows the desired holding force to be achieved smoothly without oscillation. In addition, an algorithm is found to determine contact force and contact location.

Salter, Carole A.↗

NASA NTRS1991

Transonic adaptive flutter suppression using approximate unsteady time domain aerodynamics

A digital adaptive controller is applied to the active flutter suppression problem of a wing under time varying flight conditions in subsonic and transonic flow. Linear quadratic controller gain at each time step is obtained using an iterative Riccati solver. The digital adaptive optimal controller is robust with respect to the unknown external loads. Flutter and divergence instabilities are simultaneously suppressed using a trailing-edge control surface and displacement sensing. A new transonic unsteady aerodynamic approximation methodology is developed which enables one to carry out the rapid calculation required for transonic aeroservoelastic applications. This approximation is based on a combination of unsteady subsonic aerodynamics combined with a transonic correction procedure. Aeroservoelastic transient time response is obtained using Roger's approximation, state transition matrices and an iterative time marching algorithm. The aeroservoelastic system in the time domain is modeled using a deterministic ARMA model together with a parameter estimator. Transonic flutter boundaries of a wing structure are computed, in the time domain, using an estimated aeroelastic system matrix and are in good agreement with experimental data for the low transonic Mach number range.

Pak, Chan-Gi↗