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Results for “controller tuning”

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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

An expert system to perform on-line controller restructuring for abrupt model changes

This paper presents the work in progress on an expert system utilized to tune and reconfigure airframe/engine control systems on-line in real time in response to structural or battle damage failures. The closed loop system is monitored constantly for changes in performance and structure, which detection prompts the expert system to select and apply a particular control restructuring algorithm based on the severity and type of damage. Control restructuring algorithms that have been implemented handle most failure cases involving actuator damage (control mixer) and many instances where the system dynamics are altered as well (modified control mixer).

Litt, Jonathan↗
DOE OSTI2018

Thermal profile shaping and loss impacts of strain annealing on magnetic ribbon cores

We report the use of the advanced manufacturing technique of strain annealing for nanocomposite magnetic ribbons enables control of relative permeabilities and spatially dependent permeability profiles. Tuned permeability profiles enable enhanced control of the magnetic flux throughout magnetic cores, including the concentration or dispersion of the magnetic flux over specific regions. Due to the correlation between local core losses and temperature rises with the local magnetic flux, these profiles can be tuned at the component level for improved losses and reduced steady-state temperatures. We present analytical models for a number of assumed permeability profiles. This work shows significant reductions in the peak temperature rise with overall core losses impacted to a lesser extent. Controlled strain annealing profiles can also adjust the location of hotspots within a component for optimal cooling schemes. Finally, as a result, magnetic designs can have improved performance for a range of potential operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Rigidifying Fluorescent Linkers by Metal-Organic Framework Formation for Fluorescence Blue Shift and Quantum Yield Enhancement

We demonstrate that rigidifying the structure of fluorescent linkers by structurally constraining them in metal-organic frameworks (MOFs) to control their conformation effectively tunes the fluorescence energy and enhances the quantum yield. Thus, a new tetraphenylethylene-based zirconium MOF exhibits a deep-blue fluorescent emission at 470 nm with a unity quantum yield (99.9 +/- 0.5%) under Ar, representing ca. 3600 cm(-1) blue shift and doubled radiative decay efficiency vs the linker precursor. An anomalous increase in the fluorescence lifetime and relative intensity takes place upon heating the solid MOF from cryogenic to ambient temperatures. The origin of these unusual photoluminescence properties is attributed to twisted linker conformation, intramolecular hindrance, and framework rigidity.

Wei, ZW↗
DOE OSTI2017

Optimizing the ORR activity of Pd based nanocatalysts by tuning their strain and particle size

Controlling of the particle size and surface strain is the key to tuning the surface chemistry and optimizing the catalytic performance of electrocatalysts. In this study, we show that by introducing both Fe and Co into Pd lattices, the surface strain of Pd nanocatalysts can be tuned to optimize their oxygen reduction activity in both fuel cells and Zn–air batteries. The Pd 2 FeCo/C alloy particles are uniquely coated with an ultrathin Fe 2 O 3 shell which is in situ formed during a thermal annealing treatment. The thin shell acts as an effective barrier that prevents the coalescence and ripening of Pd 2 FeCo/C nanoparticles. Compared with Pd/C, Pd 2 FeCo/C exhibits higher catalytic activity and long-term stability for the ORR, signifying changes in catalytic behavior due to particle sizes and strain effects. Moreover, by spontaneous decoration of Pt on the surface of Pd 2 FeCo/C, the Pd 2 FeCo@Pt/C core@shell structure was formed and the Pt mass activity was about 37.6 and 112.5 times higher than that on Pt/C in a 0.1 M HClO 4 and KOH solution at 0.9 V, respectively, suggesting an enhanced ORR performance after Pt decoration. More interestingly, Pd 2 FeCo@Pt/C also shows a power density of ~308 mW cm -2 , which is much higher than that of Pt/C (175 mW cm -2 ), and excellent durability in a home-made Zn–air battery.

25 ENERGY STORAGE↗
DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Coupled Lattice Polarization and Ferromagnetism in Multiferroic NiTiO 3 Thin Films

Polarization-induced weak ferromagnetism (WFM) was demonstrated a few years back in LiNbO3-type compounds, MTiO3 (M = Fe, Mn, Ni). Although the coexistence of ferroelectric polarization and ferromagnetism has been demonstrated in this rare multiferroic family before, first in bulk FeTiO3, then in thin-film NiTiO3, the coupling of the two order parameters has not been confirmed Here, we report the stabilization of polar, ferromagnetic NiTiO3 by oxide epitaxy on a LiNbO3 substrate utilizing tensile strain and demonstrate the theoretically predicted coupling between its polarization and ferromagnetism by X-ray magnetic circular dichroism under applied fields. The experimentally observed direction of ferroic ordering in the film is supported by simulations using the phase-field approach. Our work validates symmetry-based criteria and first-principles calculations of the coexistence of ferroelectricity and WFM in MTiO3 transition metal titanates crystallizing in the LiNbO3 structure. It also demonstrates the applicability of epitaxial strain as a viable alternative to high-pressure crystal growth to stabilize metastable materials and a valuable tuning parameter to simultaneously control two ferroic order parameters to create a multiferroic. Multiferroic NiTiO3 has potential applications in spintronics where ferroic switching is used, such as new four-stage memories and electromagnetic switches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A safe reinforcement learning algorithm for supervisory control of power plants

Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly. Model-free Reinforcement learning (RL) has emerged as a promising solution for control tasks due to its ability to learn from trial-and-error interactions with the environment. It eliminates the need for explicitly modeling the environment’s dynamics, which is potentially inaccurate. However, the direct imposition of state constraints in power plant control raises challenges for standard RL methods. To address this, we propose a chance-constrained RL algorithm based on Proximal Policy Optimization for supervisory control. Our method employs Lagrangian relaxation to convert the constrained optimization problem into an unconstrained objective, where trainable Lagrange multipliers enforce the state constraints. In conclusion, our approach achieves the smallest distance of violation and violation rate in a load-follow maneuver for an advanced Nuclear Power Plant design.

constrained optimization↗
NASA NTRS1994

Fuzzy self-learning control for magnetic servo system

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Tarn, J. H.↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗
NASA NTRS1997

Supplemental Control for Aircraft Riding Qualities

The concept of preview control has been applied and proven to be successful in the automotive vehicle. These same concepts are now applied to an aircraft under the assumption that exogenous inputs (wind gust, turbulence, etc. ) can be measured. A supplemental control law for surface deflection is designed to compensate for the loss in performance in the presence of atmospheric disturbances. Fuzzy logic control is employed to handle the nonlinear, time varying characteristics of the disturbance. A methodology to tune the outer loop control parameters is presented.

Williams, Rolanda S.↗
DOE OSTI2024

Hydropower Black Start: A Guidebook for Retrofitting Grid Dependent Hydropower

Not all United States (US) hydropower plants were designed to provide black start, but they are increasingly needed to uphold resilience in the evolving electric grid. This guidance is designed to help understand the minimal retrofits required for grid dependent hydropower (GDH) plants behind the point of interconnection (POI). For distribution connected hydropower plants or those with dedicated cranking paths, such upgrades can be sufficient for the plant to provide black start. For others, more coordination with the transmission system operator will be needed. This guidebook answers a number of questions relevant to retrofitting hydropower plants with black start capabilities. For example, the guidebook answers: • How flexible do the wicket gate controls need to be? • Who needs to do hydro governor model validation, why, and how? • How robust and flexible do the excitation and AVR controls need to be? • What protection settings need to be adjusted? • What relay(s) will need to be bypassed or overridden and at what risk? • What is the electrical energy demand of the station load or auxiliary power systems? • What should the strategy to energize transformer(s) along cranking path to address inrush currents be? • How should the critical load restoration be sequenced? In addition to outlining the specifications that hydropower plants need to meet for each component to be able to perform black start, this guidebook provides a set of case studies for specific upgrades needed at actual plants. Between the case studies of plants that have already performed black start retrofits and the examples of how this guidebook can be applied to scope future retrofits, five key themes have been identified for retrofit needs. 1. Protection needs “black start” mode: hydropower plants that are not designed with black start capabilities will have protections that prevent them from interconnecting to a “dead bus.” These protections will need to be overridden in every retrofit case and a separate black start mode should be established so that operators can safely switch between black start and grid connected modes, minimizing the risk to the plant. 2. Wicket gates need modern controls: digital governors accelerate the parameter tuning process and gate position sensors improve controllability, so plants with mechanical governors should be upgraded. Furthermore, a black start and islanding mode should be established for controls to maximize plant performance. 3. Robust excitation support: the DC system or excitation generator needs to be reliable enough to form and sustain the rotor electromagnetic field. These systems are typically undersized in plants that were not designed for black start, so they will need to be upgraded. 4. Turbine-governor model validation and operator training: validation of a standard hydro governor model is needed to characterize the dynamic response (i.e., inertial and primary frequency response) of the GDH. This is required for control development and old hydropower plants often have outdated or incorrect models. Operator training is also typically required to ensure the hardware retrofits are utilized correctly during the black start process. 5. Transformer and cranking path energization: any upgradation and control adjustment in front of the POI will depend upon the existing interconnection. Coordination with the transmission or distribution operator may be required.

13 HYDRO ENERGY↗
NASA NTRS1983

Microprogramed Sequencer for Tunable RF Oscillator

Standard IC chips and digitally tuned oscillator combined. Circuit consists of PROM-based microprogramed control sequencer, which drives group of diode switches to select one of eight tuning capacitors in L-C oscillator. Circuit originally designed to "dither" transmitter frequency of K-band radar at high rates finds application in other areas, as automated test equipment or computer-controlled receiver tuning.

Couch, R. H.↗
NASA NTRS2001

InP MMIC Chip Set for Power Sources Covering 80-170 GHz

We will present a Monolithic Millimeter-wave Integrated Circuit (MMIC) chip set which provides high output-power sources for driving diode frequency multipliers into the terahertz range. The chip set was fabricated at HRL Laboratories using a 0.1-micrometer gate-length InAlAs/InGaAs/InP high electron mobility transistor (HEMT) process, and features transistors with an f(sub max) above 600 GHz. The HRL InP HEMT process has already demonstrated amplifiers in the 60-200 GHz range. In this paper, these high frequency HEMTs form the basis for power sources up to 170 GHz. A number of state-of-the-art InP HEMT MMICs will be presented. These include voltage-controlled and fixed-tuned oscillators, power amplifiers, and an active doubler. We will first discuss an 80 GHz voltage-controlled oscillator with 5 GHz of tunability and at least 17 mW of output power, as well as a 120 GHz oscillator providing 7 mW of output power. In addition, we will present results of a power amplifier which covers the full WRIO waveguide band (75-110 GHz), and provides 40-50 mW of output power. Furthermore, we will present an active doubler at 164 GHz providing 8% bandwidth, 3 mW of output power, and an unprecedented 2 dB of conversion loss for an InP HEMT MMIC at this frequency. Finally, we will demonstrate a power amplifier to cover 140-170 GHz with 15-25 mW of output power and 8 dB gain. These components can form a power source in the 155-165 GHz range by cascading the 80 GHz oscillator, W-band power amplifier, 164 GHz active doubler and final 140-170 GHz power amplifier for a stable, compact local oscillator subsystem, which could be used for atmospheric science or astrophysics radiometers.

Ngo, Catherine↗
DOE OSTI2023

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. In this study, test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Closed loop lab tests of NASA's Mini-Mast

A design strategy which integrates model reduction by modal cost analysis and a multiobjective controller synthesis algorithm is used to design controllers for NASA's Mini-Mast system. The necessary modeling and control algorithms are easily programmed in Matlab standard software. Hence, this method is very practical for controller design for large space structures. The design algorithm also presents a solution for the important problem of tuning multiple-loop controllers.

Hsieh, C.↗
DOE OSTI2022

Control of Surface Chemical Reactions through Solid Stiffness

Control of surface reactions is commonly achieved by modification of surface electronic structures. Here, we discover an alternative pathway for controlling surface reactions by tuning the mechanical stiffness of the underlying material. We find that in addition to the typically assumed surface electronic contribution right at the reactive site, the contribution from the deformation of the bulk region plays a vital role in controlling surface reactions. The underlying mechanism is an elastic relaxation of the solid, which depends on the material’s stiffness and can be modified by tuning bulk stoichiometry. The effect of bulk stiffness on surface reactions has been demonstrated by considering hydrogen scission reaction and oxygen incorporation reaction during corrosion of amorphous SiC in water and air, respectively. Finally, our results imply that tuning of bulk stiffness by modifying stoichiometry can provide an effective method for controlling surface reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗