ftnbjProposal to the National Science Foundation
COVER PAGE

PROGRAM						PROGRAM SOLICITATION NO.    NSF 93-18
	SBIR - SMALL BUSINESS		
	INNOVATION RESEARCH			CLOSING DATE			June 14, 1993
NAME OF SUBMITTING ORGANIZATION (AND LEGAL NAME IF DIFFERENT)

			NeuroDyne, Inc.

NAME OF AN AFFILIATED ORGANIZATIONS (PARENT, SUBSIDIARY, PREDECESSOR)

ADDRESS OF ORGANIZATION (INCLUDE ZIP CODE)
			1 Kendall Square, Bldg. 200
			Cambridge, MA 02139
TITLE OF PROPOSED PROJECT  
Nonlinear System Identification for Real-Time Structural Health Monitoring in  Composite Materials

REQUESTED AMOUNT		PROPOSED DURATION		PERIOD OF PERFORMANCE
$ 50,000				  6 months		January 1- June 30, 1994

TOPIC NO.				TOPIC TITLE			SUBTOPIC LETTER
	21			Electrical †Communications Systems   b.
THE ABOVE ORGANIZATION CERTIFIES THAT
  											YES 	NO
1.											X
2.											X
3.											X
4.											X
5.											X
6.											X														---
TO BE COMPLETED B THE AUTHORIZED ORGANIZATIONAL REPRESENTATIVE
												X														X
 				
PRINCIPAL INVESTIGATOR/PROJECT DIRECTOR
NAME  Theresa W. Long  	SOCIAL SECURITY NUMBER OF PI/PD*    SIGNATURE 		DATE
TELEPHONE NO. (617) 252-3360   ***************           *********		***
COMPANY OFFICIAL (BUSINESS)
NAME  Donald A. Sofge		TITLE  				      SIGNATURE 		DATE
TELEPHONE NO. (617) 252-3360	Vice-President				*********		***
OTHER INFORMATION
PRESIDENT’S NAME	EAR FIRM 	HAS THIS PROPOSAL BEEN SUBMITTED	NUMBER OF EMPLOYEES (TOTAL COMPANY)
			FOUNDED	TO ANOTHER AGENCY?			Avg.previous 12 mos.4	
Theresa W. Long  	1991		YES_____ NO__X___			Currently	4
PROPRIETARY NOTICE

PROPRIETARY INFORMATION ___________________________________________
APPENDIX C
National Science Foundation
Small Business Innovation Research Program

PROJECT SUMMARY
								NSF AWARD NO.

NAME OF FIRM
				NeuroDyne, Inc.
ADDRESS
				1 Kendall Square, Bldg. 200
				Cambridge, MA 02139

PRINCIPAL INVESTIGATORS (NAME AND TITLE)
				Theresa W. Long, PI, President
				Donald A. Sofge, Co-PI, Vice-President

TOPIC TITLE								TOPIC NUMBER
  Electrical †Communications Systems					21 b
- Engineering Systems

TECHNICAL ABSTRACT (LIMIT TO 200 WORDS)

Fiber/resin composite fabrication technologies now can produce high-strength, low weight structural components, while fiber-optic sensing technology provides a means for sensing various structural properties such as stress, strain, and elasticity when these sensors are mounted on, or embedded within, a material structure.  Composite structures may degrade due to improper manufacture, hysteretic wear, impacts, and corrosion.  While embedded sensors provide a means for gathering vast quantities of data relating to the structural and dynamic history of composite structures, it is necessary to process, integrate, and interpret  this information if the embedded sensors are to be useful in determining structural integrity, using methods that can deal with the distributed and nonlinear nature of the problem. 

This research will utilize advanced neural network based techniques for modeling and analyzing dynamic structural information, as well as neural network classifiers for performing real-time sensor interpretation.  This will yield an intelligent system for processing, integrating, and interpreting embedded sensor data to give a real-time, robust, accurate measure of structural integrity for thick-section composite structures.  This project involves cooperation with McDonnell Douglas based on earlier work with them in the area of embedded sensor signal processing and neural network based classification for composite structures.  

KEY WORDS TO IDENTIFY RESEARCH OR TECHNOLOGY (8 MAXIMUM)
Nonlinear Systems	Sensor Fusion		Smart Structure	Structural Composite		
Neural Network    	Fiber Optic		Embedded Sensors

POTENTIAL COMMERCIAL APPLICATIONS OF THE RESEARCH
This Phase I effort will prove the feasibility of dynamic modeling and diagnosis to provide information on structural integrity health monitoring.  NeuroDyne and McDonnell Douglas are actively pursuing opportunities to demonstrate smart structures technology in commercial applications.  Civil structure applications being pursued include load bearing bridge structures, add-on bridge reinforcements, other civil structures, and certain Army Corps of Engineers applications.
TABLE OF CONTENTS

B. Cover Sheet	1

C. Project Summary	2

D. Identification and Significance of the Opportunity	4

E. Background, Technical Approach, and  Anticipated Benefits   	5

F. Phase I Research Objectives	6

G. Phase I Research Plan	7

H. Commercial Potential and Follow-On Funding Commitments	11	

I. Related Research	12

J. Senior Personnel	14
	1. Theresa W. Long, Principal Investigator	14
	2. Donald A. Sofge, Co-PI	15
	3. David L. Elliott, Senior Research Scientist	16

K. Consultants and Subcontracts	18

L. Equipment, Instrumentation, Computers and Facilities	18

M. Current and Pending Support of PI and Senior Personnel	18	

N. Equivalent Proposals to Other Federal Agencies	19

O. Budget	20

P. Letter of Support, McDonnell Douglas	21

Nonlinear System Identification 
for Real-Time Structural Health Monitoring 
in Composite Materials

D. Identification and Significance of the Opportunity

Composite fabrication technologies now provide a means for producing high-strength, low weight structural components with structural and material properties which can be tailored for use in specific target environments (e.g. stiffness constraints, durability, temperature and corrosion resistance). Even so, these composite structures may degrade due to improper manufacture (improper cure cycle, fiber misalignment, foreign object debris, delamination, etc.), wear from load cycling, impacts, or material corrosion (e.g. due to moisture, fuel or chemical absorption). This structural degradation may not be detectable through visual inspection.  However, with this new technology comes a new method of monitoring the structure, of great interest to civil, mechanical and aerospace engineers.

Fiber optic sensing technology provides a means for sensing stress, strain, and elasticity when these sensors are mounted on, or embedded within, a material structure.  Current composite fabrication methods have been demonstrated which allow fiber optic sensors to be embedded within a composite structure during manufacture.  These sensors maintain their viability throughout the resin cure cycle and various other production stages, and thereafter may potentially  be used for structural health monitoring.  Although a large variety of embedded fiber optic sensors have been explored, in this proposal we will focus on perhaps the simplest and most reliable methodthe fiber optic strain sensor.

While embedded sensors provide a means for gathering vast quantities of data relating to the structural and dynamic history of a composite structure, it is further necessary to process, integrate, and interpret  this information (preferably in real-time) if the embedded sensors are to be of any use in determining structural integrity.  In the proposed work we will utilize advanced techniques for processing and analyzing dynamic structural information, as well as neural network algorithms for performing real-time sensor interpretation and sensor fusion.  This effort will yield an adaptable, intelligent system for processing, integrating, and interpreting  embedded sensor data in real-time to give a robust, accurate measure of structural integrity for thick-section composite structures.

The key to understanding how embedded fiber optic sensors (in particular, strain sensors) may be used to determine structural integrity is to correlate various types of degradation with local changes in the structural dynamics of the composite structure.  Impacts, material defects, and wear all affect the structural dynamics of the composite (hence the need to monitor integrity) in a fashion which is measurable, based upon the response of the structure to normal everyday use.  Neural networks may be trained to recognize the symptoms of structural degradation through changes in the dynamic response of the composite part, and to correlate these symptoms with their root causes (e.g. impact damage, delamination, duty cycle wear).

Therefore, the task for determining structural integrity using embedded fiber optic sensors may be cast as
	(1)	determine the structural dynamics of the localized area, 
	(2)	correlate dynamic information to local structural degradation, and 
	(3)	integrate this knowledge (in real time) over the entire structure to provide input to a
 		health monitoring system.

The first task has long been studied from the viewpoint of systems theory, controls, and especially System Identification Theory.  This project will investigate the use of recurrent neural networks, coupled with more conventional autoregressive algorithms, to perform on-line system identification.  Secondly, both NeuroDyne, Inc. and McDonnell Douglas Aerospace have extensive experience in the development and application of neural network technology, both to composite structures and fiber optic sensor interpretation (see Related Work section below), and McDonnell Douglas Aerospace has extensive in-house capabilities for fabricating thick-section composites with embedded fiber optic sensors.  Third, NeuroDyne and McDonnell Douglas Aerospace have the resources and capabilities to integrate the technology developed in Phases I and II into a full-scale health monitoring system. These capabilities will be fully brought to bear to achieve successful Phase I and Phase II results, and to demonstrate an Intelligent Real-time Structural Health Monitoring System.

The Phase II partner, McDonnell Douglas Aerospace, is a leader in smart structures technology – having active internal research and several contract research programs in this area.  Periodic non-destructive evaluation (NDE) inspections of composite structures (ultrasonic imaging, thermography, eddy current, etc.) are time/labor consuming, and increase the costs associated with use of these new material technologies.  In terms of inspection, smart structure technology would greatly reduce NDE inspection requirements by reducing the frequency of NDE and by detecting new problem areas autonomously.

E. Background, Technical Approach, and Anticipated Benefits

E.1  System Identification Methodology

Neural networks present a powerful tool for the identification of multivariable nonlinear systems.  The full analytical model is often either unavailable, or available but unmanageable.  Since the system may not be fully understood, we may not know that certain parameters need to be measured even if they are available for measurement; if the nonlinear system is large in state size and spatial extent, information and decisions must be distributed over a network (e.g. identification of large flexible structures). In our approach, time-lag neural networks have already been used within a conventional modal analysis framework and shown to yield solid results for nonlinear, time-varying multi-mode system identification of a composite structure in the form of a cantilevered beam (Section I, Related Research, first item).  

Perhaps the most promising capability of neural network algorithms is the ability to learn, both off-line and on-line, by adapting weights to minimize an error criterion.  A common oversight in identifying the structural dynamics for composites using bonded or embedded sensors is the assumption of a perfect composite structure.  There often exist significant discrepancies between mathematical models and the actual physical structure due to structural flaws such as delamination.  These flaws can change the natural modal response and alter the sensor response.  Even if a structure can be manufactured perfectly, damage to the structure while in use may corrupt sensor measurements or alter the modal behavior.  The solution is an adaptive or learning system that can model the dynamics of structures with damage induced during manufacture or after installation. There are several possible approaches ([WMS]), and we have chosen a  simple one with theoretical justification Learning dynamics (state transition maps) from state information present in sampled outputs has been well demonstrated by us and others  ([Aeyels] , [Farmer] ), and its implementation via feedforward neural nets is justified by the well-known approximation theorems of Cybenko and White – Hornik – Stinchcombe, showing that smooth maps can be uniformly approximated in bounded regions by feedforward neural nets  with sigmoidal activation functions.

E. 2 Anticipated Benefits
In structural health monitoring, the large volume of incoming data from many fiber optic sensors will need inexpensive parallel computers (special VLSI chips) that can meet the speed requirements for monitoring and, where relevant, control;  while the accuracy of a neural network depends on its architecture and on the richness of the family of examples on which it is trained. The match between neural net technology and the wealth of data available from the sensors should make possible the required accuracy and margins of safety, as we plan to demonstrate.

F. 	Phase I Research Objectives

In Section G we will discuss at length the following Phase I objectives

(1)	Develop Dynamic Neural Network based system model to perform on-line identification of 
	structure.	
(2)	Develop neural network architecture for evaluating local structural integrity based upon 
	dynamic response.	
(3)	Evaluate neural network hardware requirements for real-time structural health monitoring.
(4)	Investigate embedded sensor technology for Phase II Demonstration.

Furthermore, Phase I research will seek to answer the following questions
1.	How effective is the use of dynamic neural network based modeling for deriving on-line structural dynamic models of thick-section composite structures?

2.	How critical is accurate system identification to determining structural integrity for thick composite structures, and how may neural networks be used effectively to evaluate structural health given accurate system models?
	
3.	What are the embedded sensor technology requirements for integration and implementation of neural network based structural health monitoring?

G. 	Phase I Research Plan

NeuroDyne will perform the majority of work in Cambridge, Massachusetts, with assistance from McDonnell Douglas Aerospace where indicated.  NeuroDyne and McDonnell Douglas both have experience in applying neural networks to signal processing for fiber optic sensors embedded in composite structures.  NeuroDyne has considerable expertise in applying neural networks, both static and dynamic, to on-line system identification, and McDonnell Douglas has developed extensive in-house resources for research, development, fabrication, testing, and production of smart structures which use embedded sensor technologies.  

Task 1. - Develop  Dynamic Neural Network (DNN) based System Model to perform On-line Identification of Structure 
The primary objective of this task is to develop a neural network architecture for performing on-line system identification using time-lagged networks. For general background on this approach see reference [WMS]. We will build on previous work performed jointly by NeuroDyne and McDonnell Douglas Aerospace in the development of a structural dynamics simulator for a thermoplastic composite beam with embedded sensors and actuators.  We will also make use of previous experience in the development and application of recurrent time-lag neural networks for system identification, with specific application to modeling the structural dynamics of a composite cantilevered beam.

To model the displacement of the vibrating composite structure, the neural network model should have prediction capability.  For N-stage prediction at time k of y, a sensor value (predict y(k+N)) we will need inputs y(k), y(k-1), ...,y(k-n),where k indicates time stage k, n is the window size. The rationale for using this simple structure for prediction is that (1)  for sufficiently large window size, at time k the window of sampled system outputs contains all observable state information x(k) for the nonlinear system, and (2) the neural net can, in training mode, adapt its weights to fit the map from x(k) to the predicted output y(k+N).

Figure 2 - Dynamic Network Model

Another way of looking at this architecture is as a moving-average model. For a cantilevered beam, the first 4 modes generally yield reasonable information for linearized vibration analysis.  The frequencies of the first 4 modes can be approximated based on our knowledge of the beam material.  The first 4 modes in the simulation code are shown below

Figure 3 – Modes

This frequency information can be used to give us a range of values for the size of the neural network.  The highest frequency of interest is 54.4 hertz.  The window should cover 1/2 of the wave length of wn to give us enough information for regression.  If the window size (number of samples) is too large, an averaging effect starts to set in, and the network will lose sensitivity to detail.  At a sampling rate of 1000 hz, a target window size n should be about 9 which is 1000 divided by 
(2 ¥ 54).  At a higher sampling frequency, we will be able to use a larger n.

A second factor affecting the number of samples in the window is the order of the system.  A higher order system will need a larger window size for input. The theory of observability for nonlinear systems suggests that at least twice the system order is desirable [Aeyels, Farmer]. The order of the system also dictates the number of hidden nodes.  A series of tests will be conducted to select the optimal network architecture, taking into account the nature of any output noise in the data.

Tests will be run using the composite beam simulation code.  Previous experience has suggested that the optimal two-hidden-layer network uses a window size of 9 and 25 hidden nodes per layer, however this may not be true for a damaged panel or beam.  NeuroDyne is currently developing in-house techniques which will automatically determine the appropriate network size and architecture for time-lag recurrent networks.  These techniques will be applied to the composite simulation, and optimal network architecture obtained will be compared to previous results.  The logistic function [1+ exp(-x)]-1 is the node function for all the hidden nodes. The output layer uses linear nodes.  In internal research activity, we are also evaluating alternative node functions  [Elliott 93] which have been found to provide faster convergence for modeling composite beam dynamics, as well as other applications. We have also found it desirable to provide purely linear input-output connections in parallel with the nonlinear layers.

The simulated test data will be divided into two setsa training set and testing set.  The training set will be used to train the network.  The network will then be applied to the testing set to monitor the generalization ability of the network.   

Once the  Dynamic Neural Network (DNN) has been verified to accurately model the dynamics of the composite structure, rapid modal analysis will be performed on-line using the trained network model. This modal analysis of the network model will provide the basis for classification by a second network, described in Task 2.0, for identifying structural integrity.

In order to simulate the effects of structural degradation on the beam, McDonnell Douglas will provide support to NeuroDyne in required modifications to its beam response simulation software as needed to simulate the effects of damage.  The new beam dynamics will then be re-identified by the DNN, and modal analysis will be performed as before. The goal of this task, with respect to the damaged beam, is to insure that the neural network based system identification techniques will re-identify the new beam dynamics, after which modal analysis will be performed to provide input into the structural defect classifier in Task 2.0.

Once a basic model of the structural dynamics has been achieved, it may be gradually updated over
time, or as damage occurs, based upon new sensor response data and the transient dynamic response (TDR) of the structure.  We have found it extremely useful to use a very low learning rate for such on-line learning tasks.

Task   2.  -  Develop neural network architecture for evaluating local structural integrity based upon Transient Dynamic Response  - "Smart Nodes" 
The modal analysis performed in Task 1.0 may provide a necessary basis upon which to examine local structural integrity, but on-line real-time structural integrity monitoring requires that this knowledge be integrated with real-time embedded sensor information which measures the Transient Dynamic Response (TDR) of the structure.  Hence, a neural network based classifier will integrate both system identification knowledge as well as real-time embedded sensor feedback to make a real-time determination of local structural integrity.  This local network will provide a measure of structural integrity for a localized thick-section composite.  This network will form the basic "smart node" from which information is then fed into a higher level knowledge integrator which provides sensor fusion,  redundancy, and robustness against sensor damage.  This integration is application specific and will be performed in Phase II.

NeuroDyne and McDonnell Douglas Aerospace will build upon previous experience in using neural networks to perform classification and interpretation of sensor data from fiber optic sensors embedded within composite structures.  Specifically, NeuroDyne is currently funded under a DARPA program to classify spectral data from fiber optic sensors embedded in composites to measure in situ material characteristics (such as delamination).  The McDonnell Douglas investigators supporting this effort have demonstrated that statically trained neural networks can be used to classify impact levels on composite panels based upon actual impact test data and embedded sensor signals (Figure 4). 

Figure 4 - Neural Network based Impact Prediction

Impacts, material defects, and wear all affect the structural dynamics of the composite in a fashion which is measurable based upon the response of the structure to normal everyday use (Figure 5).   Neural networks will be trained to recognize the symptoms of structural degradation based upon changes in the dynamic response of the composite part, and to correlate these symptoms with their root causes (e.g. impact damage, delamination, duty cycle wear).  The neural networks used in this effort will be multi-layer perceptrons (MLPs).  NeuroDyne and McDonnell Douglas Aerospace will modify the beam simulation to simulate the effects of damage.   A simplified Smart Node will then be trained on data gathered from the "undamaged" beam simulation, and the "damaged" beam simulation.  The Smart Node will classify the degree and type of the damage present (Figure 6).

Figure 6. A potential Smart Node architecture

The impact classifications will be trained as continuous values between 0.0 and 1.0, with 0.0 indicating that this type of damage is not detected.  The damage level detector will be a continuous valued variable normalized between 0.0 and 1.0, and will indicate the level of cumulative damage present.

In Phase II these networks will be trained off-line on data gathered from a composite panel with embedded fiber optic sensors.  The panel will be provided, and the testing performed.   An objective of this task is to identify measures of structural integrity appropriate for application of composite structure technology to various commercial areas such as load-bearing bridges, composite body panels for automobiles, and low-weight structural members for buildings, and to define a neural network architecture for classifying these defects.   

Task 3. - Real-time neural processing hardware for Real-time Structural Integrity Monitoring Neural computing hardware development is progressing at a rapid rate and as such, an investigation is required to determine the architecture most suitable for integration with the embedded sensor hardware.  Jet Propulsion Laboratory (JPL) is a leader in the development of neural computing hardware and software.  Sandeep Gulati at JPL will provide assistance to NeuroDyne for selection and Phase II development of neural processing hardware for the DNN software.

Task 4. -  Investigate embedded sensor technology for Phase II Demonstration   In this task we will investigate issues of integration of neural network technology, as described above, with embedded sensor technology for fabrication of a prototype thick-section composite with embedded sensors and real-time structural integrity monitoring in Phase II. 

McDonnell Douglas Aerospace, being engaged in all aspects of smart structure technology development, has knowledge and facilities in the specific area of embedded sensor technology and the issues involved in integration of optical sensors with computer hardware/software.  In the optoelectronics area, this includes (i) the capability of fabricating dual-splice intrinsic Fabry-Perot etalons (optical sensors),  (ii) specially-developed demodulation optoelectronics (iii) composite embedding/fabrication methods (iv) testing facilities.  In Phase I, McDonnell Douglas will provide support to NeuroDyne in fiber optic sensor selection, multiplexing architecture, data rates, synchronization of frame refresh, integration of sensors with computer hardware, thick section composite fabrication/testing, finite element modeling.

Task  5. - Final Report At the conclusion of the six month Phase I effort, NeuroDyne will provide a final report summarizing the results of Phase I and recommendations for Phase II.  This report will include results of applying the Dynamic Neural Network (DNN) architecture to identification of thick-section composite structural dynamics, structural integrity classification accuracy of the smart nodes,  and  recommendations for hardware implementation in Phase II.

H. Commercial Potential

The commercial potential of smart materials has been widely accepted in the Civil, Mechanical and Aerospace Engineering communities; and wherever safety or integrity of structure are critical some means of monitoring will be necessary. Artificial neural network technology, since it can be implemented in VLSI chips, has the likelihood of being the least expensive way of monitoring structural health. The specific research reported here has several possible Phase III commercial partners, including our Phase II partner McDonnell Douglas in aircraft and other applications. Civil structure applications being pursued include load bearing bridge structures, add-on bridge reinforcements, other civil structures, and certain Army Corps of Engineers applications.  

I. Related Research
I.1	NeuroDyne Research Projects

Distributed Neurocontrol for Active Vibration Suppression - McDonnell Douglas Aerospace, Z20418
NeuroDyne currently has a contract from McDonnell Douglas to demonstrate the use of scalable distributed neurocontrol for active vibrational damping of a thermoplastic composite aerospace structure.  This project demonstrates hierarchical learning optimal control for active vibrational suppression in "smart" composite aircraft structures.  Results by NeuroDyne have shown reduction of vibration by 96%.  In future efforts we will integrate this controller into an actual piezoelectric instrumented composite structure.

IR Spectroscopy Sensor for On-line Inspection of Moving Materials - ARPA Innovative process automation methods are being developed under ARPA-UWO to achieve cost-effective manufacture of high-quality thick-section composite structures.  Recent studies suggest that the key to reducing the cost of composites lies in the use of processes which avoid the autoclave.  The fiber placement process achieves this through automated on-line consolidation of thermoplastics or on-line cure of thermoset composites.

Process automation methods have been limited by the lack of adequate sensors and sensor processing methods.  NeuroDyne will utilize an in-house developed transient IR sensing technique for the on-line inspection of continuous moving solid materials at rates up to 78 ft/min.  Intelligent processing of the spectral data through a wavelet/neural network based classification algorithm will allow real-time interpretation of material conditions  The resulting product will be an automated computer based system for the real-time, in-situ characterization of process states and resin chemistry for fiber placement of thermosets and thermoplastic materials.

Autonomous Neural Control of Space Platforms - AF Phillips Laboratory, F29601-93-C-0094    The autonomous control of flexible space structures will require a distributed computational architecture that provides the ability to perform system identification and adaptive control after orbital deployment.  Neural network based supervised learning algorithms and hardware provide a beneficial tool for reducing the need for a priori knowledge of the structure's behavior through rapid system identification and health monitoring on-line.  Furthermore, the combined use of neural networks and optimal control presents a stochastic nonlinear learning control architecture that provides the ability to rapidly adapt on-line to uncertainty in the structure's behavior.  

In this effort our team will develop and apply real-time system identification and learning optimal control for control/structural interaction (CSI) of the Phillips Laboratory Planar Articulating Controls Experiment (PACE) test bed.  Final goal of this effort will be integration of controls and demonstration of vibration damping and precision pointing for the PACE structure.

Optimal Decentralized Control using Neural Networks - NSF, ECS 9119918
NeuroDyne developed some of the basic techniques referenced herein under a grant from the Neuroengineering Program of the National Science Foundation, Electrical and Communications Systems Division, for the research and development of optimal decentralized control architectures which are necessary to solve difficult real-world distributed control problems such as control and optimization of large-scale distributed systems.  This work investigated and developed a neural network based decentralized control law which substantially improved control system bandwidth over the more conventional centralized control architectures.  Results have developed and compared RBF, CMAC, conjugate gradient backpropagation trained MLPs, conventional MLP network approaches for distributed system identification, and found an improvement in controller bandwidth in excess of an order in magnitude over the best method published previously or since for a benchmark decentralized control problem trajectory control of a 3-link Puma arm. 

Neuroengineering Analysis and Design Tool for Complex Coolant Systems - NSF ECS
NeuroDyne currently has a contract from the National Science Foundation Electrical and Communications Systems Division for the research and development of optimal decentralized flow modeling and control architecture for complex coolant systems, such as the NASP active cooling system.  Decentralized control architectures are necessary to solve many difficult real-world distributed control problems such as active vibrational damping control for aerospace structures or distributed flow control in large scale chemical processes. Under this program, neural network based decentralized controllers will be developed which substantially simplify and improve control of complex non-linear multivariate systems over the more conventional centralized control architectures.

Learning Optimal Control System for Fiber Placement -  DARPA 9210965-1
Significant progress has been achieved in the development of new equipment and modification of existing filament winding equipment to accommodate in situ fabrication of thermoplastic parts.  Advantages of on-line consolidation through tow placement are continuous winding of thick-walled parts, elimination of oven or autoclaving curing, low void content, and less material scrap.  One of the challenges to widespread use of tow placement is the reduction of production costs to be competitive with that of epoxy systems.  A key to reducing manufacture costs is the increase of fabrication speeds while maintaining process quality. A robust control system for rapid consolidation of composite material requires the ability to obtain accurate feedback information from the process and the ability to adapt to unforeseen changes in the process dynamics.  Under a recently awarded DARPA contract, NeuroDyne is applying internally developed innovations in learning-optimal control to learn tow placement process dynamics on-line and optimize control of the process.  NeuroDyne will also use new innovations in adaptive signal processing to optimally filter process feedback to provide accurate on-line Non-Destructive Evaluation (NDE) measures to ensure the quality manufacture of composite parts.

Enhanced Performance Seeking Control using Adaptive Nonlinear State Estimation - NASA Lewis Research Center, NAS3-26911
Under a program sponsored by NASA-Dryden Research Center, the Performance Seeking Control (PSC) system uses on-board models of the inlet, engine, and nozzle to optimize total propulsion system performance in-flight.  This system has resulted in significant improvements in maximum thrust (10%) and fuel consumption (7%) during F-15 test flights.  However, there exists a great need for a nonlinear adaptive estimation component to adapt the on-board models for off-nominal behavior such as engine deterioration and engine-to-engine variations.

Under contracts from NASA Lewis and Dryden Research Centers, NeuroDyne and McDonnell Douglas Corporation (MDC) are extending the current PSC effort to develop a nonlinear adaptive engine estimation component for the PSC system using a combination of neural network based system identification and optimal filtering methods.  This component will input current state measurements and provide estimates of low and high spool efficiency adders, fan airflow adder, compressor airflow adder, and high turbine area adder.  This effort is also examining parallel processing hardware, developed by Jet Propulsion Laboratory (JPL) for NASA Lewis, which is compatible within the PSC Vehicle Management System (VMS) flight computer.  The resulting system will be integrated into a new RISC processor based flight computer and tested at MDC. The final goal of this effort is integration into the NASA Dryden F-15 test vehicle for flight test in 1994.

I.2 References

[Aeyels] D. Aeyels, ``Generic observability of differentiable systems,''SIAM J. Control Opt., 
	vol. 19 (1981) 595-603.
[Farmer] J.D. Farmer, J.J. Sidorowich, ``Exploiting chaos to predict the future and reduce noise,''  
	Proc. First Workshop on Computational Learning Theory, MIT, 1988.
[WMS] P.J. Werbos, T. McAvoy, T. Su,``Neural networks, system identification, and control in the 
	chemical process industries,'' (pp. 283-356) in Handbook of Intelligent Control ed. by 
	D.A. White, D.A. Sofge, New YorkVan Nostrand Reinhold, 1992. 
[Ell93] D.L. Elliott,  “A better activation function for artificial neural networks,” Report TR 93-9, 
	Institute for Systems Research, University of Maryland, Jan. 29, 1993.

J. Principal Investigator and Senior Personnel

Theresa Wenwei Long - Principal Investigator, President, NeuroDyne Inc. 

Education
	Ph.D., Mechanical Engineering, University of South Carolina, 1988. M.S., Mechanical Engineering, University of South Carolina, 1985. B.S., Mechanical Engineering, State University of New York at Buffalo (SUN), 1977. B.A., History, University of Taiwan, 1975. 

Positions
(1991 - Present)  President of NeuroDyne, Inc.
(1989 - 1991) Control Engineer, Laser Engineering Division Lawrence Livermore National 	Laboratory 
(Spring 1989) Research Engineer, Westinghouse Savannah River Company 
(1977 - 1982)  Project Engineer, Exxon Research and Engineering Company 

Research Experience
	Dr.  Long directs the operations of NeuroDyne Inc.  and leads the development of intelligent control, system identification,  and measurement research.  She is also a Visiting Scientist at the Massachusetts Institute of Technology Artificial Intelligence Laboratory where she is focused upon nonlinear system identification and control research using neural enhanced dynamic programming methods.  She was the principal investigator of NSF funded research to investigate a neural network enhanced optimal control method for the decentralized control of a six-link manipulator.  She has 15 years of industry experience in practical engineering problem solving in control systems development at Lawrence Livermore National Laboratory, Westinghouse, and Exxon. 
	While at Lawrence Livermore National Laboratory, Dr. Long was responsible for the analysis, design, and development of advanced control systems for laser technology, tracking mechanisms, manufacturing processes, and robotic gantry systems.  Most recently, she led the development of wavefront and alignment control of laser technology and temporal filtering of wavefront measurement data. While at Livermore, she also developed the ACE IDEA system for satellite attitude control and despinning.  As a research engineer at Westinghouse, Dr.  Long designed the RAPT and TARDIS optimal control systems, and as a project engineer at Exxon, she gained experience on a variety of environmental projects including air pollution control. She has numerous publications in the area of optimal control theory and applications, such as

(with C.K. Carrington) “A new nonlinear optimal control methodthe tracking and relative dimension imbedded system,” Int. J. Control, vol. 34, no. 2, 1991.

“The RAPT control method for nonlinear systems,” ASME J. of Dynamic Systems, Measurement
and Control, December 1991.

 “ACE IDEA approach for satellite despinning control,” Proc. 28th IEEE Conf. on Decision and Control, Tampa, FL, December 1989.

“TARDISa two point optimal control technique for nonlinear systems,” Proc. AIAA/AAS Astrodynamics Conference, Minneapolis, August 1988.

“Satellite attitude control in the equivalent axis frame,” to appear, Acta  Astdronautica, the Journal of the International Academy of Astronautics.

Collaborators and Advisors
Graduate Advisor – Prof. Connie K. Carrington, Dept. of Mechanical Engineering, University of South Carolina

Christopher Atkeson, MIT
Andrew Moore, MIT
Donald Sofge, NeuroDyne, Inc.
Emil Hanzevack, U. of South Carolina

Donald A. Sofge, Co-Principal Investigator, Vice-President, NeuroDyne Inc. 

Education
	M.S., Electrical Engineering, University of Florida, 1988. B.S., Computer Science, University of North Florida, 1986.

Experience
	Mr. Sofge currently leads the development of intelligent control systems at NeuroDyne and is a Visiting Scientist at the Massachusetts Institute of Technology.. As the principal investigator of the Intelligent Design and Manufacture of Thermoplastic Composites IRAD project at McDonnell Douglas, Mr. Sofge led the research and development of intelligent systems for NonDestructive Evaluation (NDE), process control, and process modeling. He is a leader in the development and application of CMAC neural networks and is the co-inventor of the Fiber Placement Neurocontrol System for Composite Manufacturing as well as the Learning Kalman Filter (patents pending). Mr. Sofge also served as Principal Investigator for the DARPA sponsored effort "Learning-Optimal Control for Fiber Placement Processing", and acted as Principal Investigator for the DARPA sponsored effort "Adaptive Diagnostics and Control of the Plasma Etching Process".  Mr. Sofge leads the technical development at NeuroDyne under another effort in applying wavelet and neural network based classification methods to fiber-optically sensed IR spectral signatures applied to composite processing.
	Mr. Sofge was a session chairman for the 1991 Neural Information Processing Systems conference (NIPS) and was co-chairman of the NSF - McDonnell Douglas - Washington University Workshop on Aerospace Applications of Neurocontrol (1990). His publications in the area of intelligent control include

Co-Editor (with D.A. White), Handbook of Intelligent Control, New YorkVan Nostrand Reinhold, 1992.

(with D.A. White) “Learning-optimal control for manufacturing,” in the cited Handbook of Intelligent Control.

“Differential CMAC models for on-line learning control,” Proc. NASA-AIND Workshop on Neural Networks, Auburn, AL, February 1992.

(with D.A. White) “Neural network based process control,” Proc. 29th IEEE Conf. on Decision †Control, Honolulu, Hawaii, December 1990.

(with D.A. White) “Neural network based control for composite manufacturing,” in Intelligent Processing of Materials, New YorkASME Publications, 1990.

Collaborators and Advisors
Graduate Advisor – Prof. John Staudhammer, Dept. of Electrical Engineering, University of Florida

David A. White, NDI
David L. Elliott, NDI and Washington University
Christopher Atkeson, MIT
Andrew Moore, MIT
Theresa Long, NDI.

David L. Elliott, Senior Scientist, NDI
 
Education

	Ph.D. 1969	Engineering (Mathematical Theory of Systems) University of California, 
			Los Angeles;
	M.A. 1959, Mathematics, University of Southern California.
	B.A.   1953	Mathematics, Pomona College.

Academic Appointments
	1980–1994	Professor of Mathematical Systems (on pre-retirement leave 1992-94)
	 Washington University, St. Louis;
	July 1992– Visiting Senior Research Scientist (no salary), Institute for Systems Research, 
			University of Maryland, College Park, Maryland;
	1992 – Senior Research Scientist, NeuroDyne, Inc.;
	1971–	Faculty member, Department of Systems Science and Mathematics,Washington 
		University;
	1987–1989	Program Director for Systems Theory, National Science Foundation;
	1986–1987	Visiting Professor of Electrical Engineering, University of California, Los 
			Angeles;
	Fall 1979	Visiting Associate Professor, Lefschetz Center for Dynamical Systems, 
			Brown University; 
	1969–1971	Lecturer, Department of System Science, University of California, Los 
			Angeles;
	Summer 1969	Senior Engineer, NASA Jet Propulsion Laboratory, Pasadena
	1955–1969	Mathematician, U.S. Naval Ocean Systems Center (Pasadena, California)

Recent Research Grants

•   NSF Grant No. ECS-9022539,``Workshop on Aerospace Applications of Neurocontrol''
•   NSF Grant No. DMS-9105272,``Mathematical Sciences Research Environment'' 
	(transferred to C.I. Byrnes)
•   NSF Grant No. ECS-9204612,``Workshop on Nonlinear Control Systems, Washington 
	University, St. Louis, MO, May 27--May 31, 1992''
•   NSF Grant No. ILI-9251452, “Systems Engineering Laboratory,” (transferred to H. Mukai) 
•   NSF Grant No. ECS-9223463, “Workshop on Neuroengineering, University of Maryland, 
	Oct. 25-27, 1992” 
No responsibilities for the above grants remain at this time; nor was PI salary involved.
 
Graduate Advisors and Research Collaborators
Doctoral Advisor A.V. Balakrishnan, UCLA.

 Christopher I. Byrnes, Washington University
 W.P. Dayawansa, University of Maryland
 Babu Joseph, Washington University
 Hiroaki Mukai, Washington University
 Clyde Martin, Texas Tech University
 Donald Sofge, Massachusetts Institute of Technology and NeuroDyne, Inc.
 David White, Massachusetts Institute of Technology and NeuroDyne, Inc.
Theresa W. Long,Massachusetts Institute of Technology and NeuroDyne, Inc.
 William Weijian Zhang, University of Arizona

Relevant Publications
 
	•  (with N.R. Vemula and A.M. Engebretson) ``Models for the human throat-wall and a 
	study of the vocal tract from input/output measurements,'' , Proc. 1978 IEEE Conf. on 
	Decision and Control, San Diego, California, pp. 946--948, Jan. 1979.
	•  (with R.J. Su and T.J. Tarn),``Topological considerations in nonlinear system 
	realization,'' Proc. 17th Allerton Conf. on Communication, Control and Computing,
	 pp.540--545, October 1979.
	•  ``Stochastic time-optimal control problems,'' (with Weijian Zhang) IEE Proceedings, 
	Part D Control Theory and Applications, Vol. 135, pp. 395--404, 1988.
Honors
	Fellow, IEEE, for contributions to geometric system theory (January 1985).
Professional Registration Control Systems Engineer No. 690 (California)
Professional Societies IEEE , AMS, MAA, SIAM, Sigma Xi
Professional Activities
	•  Associate Editor at Large, IEEE Transactions on Automatic Control,1990–
 	•  Associate Editor, Systems and Control Letters, 1990–
	• Previous editorships Mathematical System Theory, SIAM Review. Chaired Invited
	 Sessions for AMS, IFAC Congresses, CDC, SIAM.

K. Consultants and Subcontracts

	No consultants or subcontracts will be used in Phase I.
 
L. Equipment, Instrumentation, Computers and    Facilities

A. NeuroDyne Facilities
NeuroDyne's facilities include office and laboratory space near the MIT campus, UNIX and Macintosh Workstations with access to IBM and VMS systems, as well as access to a CM-2 for special purpose computing needs. Peripherals including laser printers, modems (including digital fax capability), and Internet access are also available. NeuroDyne also has the facilities necessary to integrate signal processing hardware into one of the aforementioned computing platforms to deliver a complete neural network hardware system. 
	The computational work will be performed on Macintosh Workstations using Think C software, and Unix Workstations in ANSI-C.
 
B. McDonnell Douglas Facilities

McDonnell Douglas possesses a wide range of facilities for development and testing of smart structures, with active research and development programs in structural composites, fiber optic sensing techniques, and smart structure programs throughout the corporation.  Smart Structure research and development activities are primarily supported through the Fiber Optic Sensor Development Laboratory. 

The Fiber Optic Sensor Development Laboratory offers unique facilities and equipment to construct sensors and quantify their performance.  Both phase-based and amplitude-based sensors are routinely built for use in monitoring strains induced by mechanical, thermal, and acoustic loads. The Laboratory possesses numerous InGaAsP and GaAlAs semiconductor laser diodes with output wavelengths ranging from 850nm to 1550nm with output powers up to 20 milliwatts.  Two high-powered 1300nm LEDs are also available, as are numerous InGaAs and silicon photodiodes covering 400nm to 1600nm wavelengths.  Various other equipment available provides capabilities for splicing and fusing sensors, spectrum analysis, high resolution power output measurements, and mechanical and ultrasonic cleaving.

M. Current and Pending Support of PI and Senior
Personnel 
L.1	Theresa W. Long, Principal Investigator - Dr. Long has the following support
1.	Current Support
	a.	"Adaptive Diagnostics and Control of the Plasma Etching Process", ARPA 
		Grant DAAH01-93-C-R072, 12/16/92 – 07/16/93, 0.66 months.
	b.	"Integrated Control System for Aerospace Plane Using Stochastic Nonlinear 
		Optimal Control", NASA Langley Grant NAS1-19892,  01/21/93 - 8/21/93,
		0.72 months.
	c.	"Performance Seeking Control Using Neural Network Based State Estimation",
		NASA Lewis Grant NAS3-26911,  12/18/92 - 7/18/93, 0.69 months.
	d.	"Neuroengineering Analysis and Design Tool for Complex Coolant Systems",
		NSF SBIR Grant III-9260672, 01/01/93 - 09/30/93, 2.0 months.
	e.	"Autonomous Neural Control of Space Platforms", Air Force Grant F29601-93-C-
		0094, 04/09/93 - 11/09/93, 1.08 months.
	f.	"Neural Network Technology for Jet Engines", Air Force Grant F33615-93-C-
		2315, 04/15/93 - 11/15/93, 0.78 months.
2.	Pending Support
	a.	“Reducing Pollution and Increasing Fuel Economy Using Intelligent Control",  
		NSF Grant ECS-9216530, est. 06/15/93 – 06/13/96, 0.5 month/year.
	b.	"Learning-Optimal Control for Unmanned Supermaneuverable Technologies",
		Navy SBIR Grant, est. 06/15/93 - 01/15/94, 0.66 months.
	c.	"Learning-Optimal Control System for Fiber Placement Processing", ARPA-UWO
		SBIR Phase II proposal, est. 07/01/93 - 07/01/95, 1.72 month/year.

L.2	Donald A. Sofge, Co-Principal Investigator - Mr. Sofge has the following support
1.	Current Support
	a.	"Neural Networks Determine Degree of Cure in High Rate Composite
		Fabrication Processes",  ARPA Grant DAAH01-93-C-R047, Subcontract
		29643, 4/1/93-8/15/93,  1.4 months.
2.	Pending Support
	a.	“Reducing Pollution and Increasing Fuel Economy Using Intelligent Control",  
		NSF Grant ECS-9216530, 04/15/93 – 04/13/96, 1.1 month/year.
	b.	"An Intelligent Scalable Control Architecture for Distributed Nonlinear Systems",
		ARPA Log # 9310245, est. 06/16/93 - 01/16/94, 3.0 months.
	c.	"Learning-Optimal Control System for Fiber Placement Processing", ARPA-UWO
		SBIR Phase II proposal, est. 07/01/93 - 07/01/95, 1.88 month/year.
	
L.3	David L. Elliott, Senior Research Scientist - Dr. Elliott has the following support
1.	Current Support
	a.	"Adaptive Diagnostics and Control of the Plasma Etching Process", ARPA 
		Grant DAAH01-93-C-R072, 12/16/92 – 07/16/93, 0.24 months.
	b.	"Integrated Control System for Aerospace Plane Using Stochastic Nonlinear 
		Optimal Control", NASA Langley Grant NAS1-19892,  01/21/93 - 8/21/93,
		0.42 months.
	c.	"Performance Seeking Control Using Neural Network Based State Estimation",
		NASA Lewis Grant NAS3-26911,  12/18/92 - 7/18/93, 0.36 months.
	d.	"Autonomous Neural Control of Space Platforms", Air Force Grant F29601-93-C-
		0094, 04/09/93 - 11/09/93, 0.18 months.
	e.	"Neural Network Technology for Jet Engines", Air Force Grant F33615-93-C-
		2315, 04/15/93 - 11/15/93, 0.66 months.
2.	Pending Support
	a.	“Reducing Pollution and Increasing Fuel Economy Using Intelligent Control",  
		NSF Grant ECS-9216530, 04/15/93 – 04/13/96, 0.83 month/year.
	b.	"An Intelligent Scalable Control Architecture for Distributed Nonlinear Systems",
		ARPA Log # 9310245, est. 06/16/93 - 01/16/94, 0.18 months.
	c.	"Learning-Optimal Control for Unmanned Supermanueverable Technologies",
		Navy SBIR Grant, est. 06/15/93 - 01/15/94, 0.24 months.
	d.	"Learning-Optimal Control System for Fiber Placement Processing", ARPA-UWO
		SBIR Phase II proposal, est. 07/01/93 - 07/01/95, 0.3 month/year.

N. Equivalent Proposals to Other Federal Agencies 

	No equivalent proposal has been granted or is pending before any Federal Agency.

O. Budget

P.  Prior Phase II Awards

	NeuroDyne has received fewer than 15 Phase II SBIR awards.

Q. Letter of Support, McDonnell Douglas