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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
DOE OSTI2026

Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning

Molybdenum carbide nanoparticles (α-MoC 1−x NPs) are promising catalysts that offer noble-metal-like performance at lower cost. We report a mild continuous-flow synthesis of α-MoC 1−x NPs from Mo(CO) 6 , coupled with in-line spectroscopic monitoring and machine learning (ML)-based analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron ML model was found to accurately deconvolute complex, nonlinear spectral patterns, enabling identification of a two-step reaction pathway, involving precursor conversion to an amorphous intermediate followed by intraparticle crystallization to α-MoC 1−x NPs, with the first step being rate limiting. Ex situ small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) validation confirm the predicted concentration profiles and crystallization behavior. This integrated approach showcases how ML can empower insights into NP nucleation and growth, paving the way for self-driving, flow-based platforms for NP synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
NASA NTRS2005

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.

Goetz, Scott J.↗
DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2016

My Internship at NASA

My name is Isaac Lopez and I am a junior at the University of Houston majoring in Mechanical Engineering Technology. I will be completing my first tour at the NASA-Johnson Space Center ("JSC") as a Mechanical Engineer within the Human Interfaces Branch. Throughout my tour, I was given the opportunity to work on multiple projects that have expanded my knowledge and interest in acoustics and engineering design. One of the projects I worked on at JSC consisted of doing acoustic simulation of the EVA comm. cap. While working on the comm. cap headset, my main duty consisted of simulating the acoustics of the headset to find a solution to the condensing water that can accumulate and block the acoustic tube, causing attenuation or complete loss of audio in one ear for an astronaut using the EVA. For this project, I had to create a Creo model of the comm. cap so that I would be able to import it into Comsol for acoustic simulation. I also had the opportunity to design a portable and lightweight beam degrader for the EEE Parts and Radiation team. With the help of Creo, I was able to make a CAD design and put together a small working prototype for the radiation team to demonstrate the capabilities that the beam degrader had. In addition to these projects, JSC allowed me to work closely on projects with other interns. I had the opportunity to help another intern with his acoustic diverter, intended to improve the sound quality in Node 1 of the ISS. During this project, I helped with some of the acoustic testing inside the anechoic chamber as well as helping record data during testing at the ISS mock up. During the course of my first tour, I was able to learn and continually improve on my CAD drafting skills. With each project I worked on, I acquired new ways to create and improve various designs with various constraints. Furthermore, I also had the opportunity to work with electrical engineers and learn about the electronic components that would provide control of the beam degrader functions. I was able to take the knowledge, skills and designs I had acquired during my tour at JSC (such as Finite Element Analysis) and apply them to my work on the Comsol Multiphysics software for acoustics simulation. I was also able to use it to do some stress analysis on a proposed design for a proposed Habitat structure. My time at JSC has been invaluable as it helped me grow and more fully-comprehend my major as well as spark an interest in acoustics and how much it relates to mechanical engineering. This internship has taught me that engineering is much more than a profession - it is embedded in our daily lives as a puzzle that requires critical thinking as an everyday challenge that makes life more interesting.

Lopez, Isaac↗
NASA NTRS2016

Smart Congestion Control for Delay- and Disruption Tolerant Networks

In this paper, we propose a novel congestion control framework for delay- and disruption tolerant networks (DTNs). The proposed framework, called Smart-DTN-CC, adjusts its operation automatically as a function of the dynamics of the underlying network. It employs reinforcement learning, a machine learning technique known to be well suited to problems in which the environment, in this case the network, plays a crucial role; yet, no prior knowledge about the target environment can be assumed, i.e., the only way to acquire information about the environment is to interact with it through continuous online learning. Smart-DTN-CC nodes get input from the environment (e.g., its buffer occupancy, set of neighbors, etc), and, based on that information, choose an action to take from a set of possible actions. Depending on an action’s effectiveness in controlling congestion, it will be given a reward. Smart-DTN-CC’s goal is to maximize the overall reward which translates to minimizing congestion. To our knowledge, Smart-DTN-CC is the first DTN congestion control framework that has the ability to automatically and continuously adapt to the dynamics of the target environment which allows Smart-DTNCC to deliver adequate performance in a variety of DTN applications and scenarios. As demonstrated by our experimental evaluation, Smart-DTN-CC is able to consistently outperform existing DTN congestion control mechanisms under a wide range of network conditions and characteristics.

Hirata, Celso M.↗
DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗
NASA NTRS2017

The Curiosity Effect

This conference aims to improve how we learn through integrative project and discovery-based methods. My talk highlights areas in my experience as a scientist, and most recently working for our national space agency, NASA, where we work in teams with a "discovery-based" mindset. When you demonstrate broad curiosity, you become open to different viewpoints and ways to approach and manage situations. Sometimes working only from "what you have been trained to do" or "what you know" is not enough, especially when the rules may be changing. Increasing our openness in our learning, and sharing what we know, can lead to a more diverse and innovative community, solving problems in new ways, overcoming resistance to new ideas, and hopefully creating a dynamic and faring-forward society. Let us not kill curiosity, at any age, in any situation. Let us remind ourselves, at any time, in any circumstance, to continue to learn, to mentor, to stimulate, to engage and reconnect with that "open sense of possibility."

STEM↗
NASA NTRS2016

Additive Manufacturing, Design, Testing, and Fabrication: A Full Engineering Experience at JSC

I worked on several projects this term. While most projects involved additive manufacturing, I was also involved with two design projects, two testing projects, and a fabrication project. The primary mentor for these was Richard Hagen. Secondary mentors were Hai Nguyen, Khadijah Shariff, and fabrication training from James Brown. Overall, my experience at JSC has been successful and what I have learned will continue to help me in my engineering education and profession long after I leave. My 3D printing projects ranged from less than a 1 cubic centimeter to about 1 cubic foot and involved several printers using different printing technologies. It was exciting to become familiar with printing technologies such as industrial grade FDM (Fused Deposition Modeling), the relatively new SLA (Stereolithography), and PolyJet. My primary duty with the FDM printers was to model parts that came in from various sources to print effectively and efficiently. Using methods my mentor taught me and the Stratasys Insight software, I was able to minimize imperfections, hasten build time, improve strength for specific forces (tensile, shear, etc...), and reduce likelihood of a print-failure. Also using FDM, I learned how to repair a part after it was printed. This is done by using a special kind of glue that chemically melts the two faces of plastic parts together to form a fused interface. My first goal with SLA technology was to bring the printer back to operational readiness. In becoming familiar with the Pegasus SLA printer, I researched the leveling, laser settings, and different vats to hold liquid material. With this research, I was successfully able to bring the Pegasus back online and have successfully printed multiple sample parts as well as functional parts. My experience with PolyJet technology has been focused on an understanding of the abilities/limits, costs, and the maintenance for daily use. Still upcoming will be experience with using a composite printer that uses FDM technology to print plastic while laying an internal filament of Kevlar or carbon-fiber inside the printed material. It has been incredible being exposed to this range of technologies and I feel very fortunate to be ready for virtually any kind of printing technology I come across in the future. Design work played a part in my internship this term as well. Working with Hai Nguyen, I was able to design a set of testing tips and a test frame for use with an Arcjet. The testing tips will be made of several different materials that will possibly be used in the heat shield of the Orion space craft. These designs included technical drawings that were presented to the fabrication shop. The frame design was created from 80/20 (a popular brand of frame construction equipment) and included an order form with pricing for fabrication. An independent design was also done for the virtual reality lab. This design was to create a hand-controller based on a previous design. This final design was sent directly to a 3D printer without technical drawings. Overall, my design work has given me experience with using 80/20, helped improve my CAD (Computer Aided Design) proficiency, and increased my knowledge of how to set up technical drawings for fabrication. The final role I have played in this internship has been to assist with testing of the inflatable technology materials working Khadijah Shariff. I began the internship assisting with permeability testing with the initial plan to continue the testing independently after training. Unfortunately, the testing apparatus suffered a technical failure and had funding pulled which cancelled that portion of the project. Further testing with inflatable technology continued with tensile testing of various stitching methods. This testing took place over a two-days and concluded successfully. Final testing was to be more tensile testing but of straps used to connect various inflatable sections. Unfortunately, the needed grips for the tensile tests could not be located and put the testing on hold. It is possible this round of testing will still take place by the end of the internship if the grips can be found. Overall, this portion of the internship has helped me become familiar with one kind of permeability test as well as a popular tensile/compression testing machine. Finally, I also had the chance to be trained using a metal lathe for making very small tips for a soldering iron. These tips will be used to melt threaded brass inserts into 3D printed plastic. Working with James Brown, I was able to successfully machine a brass rod down to as little as 0.064 inches plus or minus 0.001 inches. It was very rewarding to learn how to best use the machine and become familiar with a skill that will undoubtedly be used again in the future. I have been told by several professional engineers that learning to use a lathe and mill will be invaluable skills in the field. This knocks 50 percent of that goal off and I look forward to learning the mill at some point in the future. As is apparent with this list of projects, my internship was not focused on a single over-arching goal. Instead, I was able to gain experience in a myriad of very different areas. I feel like my time here was spent bouncing from one project to the next. Though sometimes difficult to switch gears, it was very rewarding to be a part of so much in so little time. My career and education will be positively impacted by what I have learned at JSC. My experience with 3D printing has improved my ability to handle many issues that may come up in the future with multiple different technologies. The design work I took part in, especially creating technical drawings, will help me better present designs to any engineer or shop I will encounter. My testing experience has helped me become familiar with a popular kind of tensile test machine that will likely be similar to the kinds I will encounter in the future. Finally, my experience with fabrication has given me a rare opportunity, as an engineer, to take part in the fabrication of a part. This experience will help me better tailor my future designs for the manufacturing process and has given me an appreciation for detailed/delicate machining work. My experience at JSC has been successful and will continue to assist me for a long time within the engineering field.

Zusack, Steven↗
NASA NTRS2010

Things That Work: Roles and Services of SPDF

The current Heliophysics Science Data Management Policy (HpSDMP) defines the roles of the Space Physics Data Facility (SPDF) project as a heliophysics active Final Archive (aFA), a focus for critical data infrastructure services and a center of excellence for data and ancillary information services. This presentation will highlight (1) select current SPDF activities, (2) the lessons we are continuing to learn in how to usefully serve the the heliophysics science community and (3)SPDF's programmatic emphasis in the coming year. In cooperation with the Heliophysics Virtual discipline Observatories (VxOs), we are working closely with current, and with upcoming missions such as RBSP and MMS, to define effective approaches to ensure the long-term availability and archiving of mission data, as well as how SPDF services can complement active mission capabilities. We are working to make the Virtual Space Physics Observatory (VSPO) service comprehensive in all significant and NASA relevant heliophysics data. We will highlight a new CDAWeb interface, a faster SSCWeb, availability of our data through VxO services such as Autoplot, a new capability to easily access our data from within IDL and continuing improvements to CDF including better handling of leap seconds.

McGuire, R. E.↗
DOE OSTI2025

Converting sWeights to probabilities with density ratios

The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The sPlot formalism is a common tool used to isolate distributions from different sources. However, the negative sWeights produced by the sPlot technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert sWeights to event probabilities, which we call drWeights. The drWeights can then be applied to produce the distributions of interest and are consistent with direct use of the sWeights. This article will also show how decision trees are particularly well suited to convert sWeights, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial drWeights are reweighted by an additional converter gives substantially better results.

Glazier, Derek I. [Univ. of Glasgow, Scotland (Uni↗
DOE OSTI2025

Automatic speech recognition predicts contemporaneous earthquake fault displacement

Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

58 GEOSCIENCES↗
NASA NTRS1990

Neural networks for self-learning control systems

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Nguyen, Derrick H.↗