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NASA NTRS1992

A neuro-fuzzy architecture for real-time applications

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

Ramamoorthy, P. A.↗

NASA NTRS1992

On the use of ANN interconnection weights in optimal structural design

The present paper describes the use of interconnection weights of a multilayer, feedforward network, to extract information pertinent to the mapping space that the network is assumed to represent. In particular, these weights can be used to determine an appropriate network architecture, and an adequate number of training patterns (input-output pairs) have been used for network training. The weight analysis also provides an approach to assess the influence of each input parameter on a selected output component. The paper shows the significance of this information in decomposition driven optimal design.

Hajela, P.↗

NASA NTRS1992

Application of neural networks to prediction of advanced composite structures mechanical response and behavior

Two types of neural networks were used to evaluate acousto-ultrasonic (AU) data for material characterization and mechanical reponse prediction. The neural networks included a simple feedforward network (backpropagation) and a radial basis functions network. Comparisons of results in terms of accuracy and training time are given. Acousto-ultrasonic (AU) measurements were performed on a series of tensile specimens composed of eight laminated layers of continuous, SiC fiber reinforced Ti-15-3 matrix. The frequency spectrum was dominated by frequencies of longitudinal wave resonance through the thickness of the specimen at the sending transducer. The magnitude of the frequency spectrum of the AU signal was used for calculating a stress-wave factor based on integrating the spectral distribution function and used for comparison with neural networks results.

Cios, K. J.↗

NASA NTRS1992

Quadratic stabilization and tracking - Applications to the benchmark problem

Recent results regarding quadratic stabilization and disturbance attenuation are applied to the benchmark problem. The authors investigate the minimum achievable levels of disturbance attenuation for different disturbance inputs and controlled outputs. They then design a feedforward tracking controller which tracks step inputs while maintaining the disturbance attenuation properties of the regulator.

Schmitendorf, W. E.↗

NASA NTRS1993

Active control of fan noise from a turbofan engine

A three channel active control system is applied to an operational turbofan engine in order to reduce tonal noise produced by both the fan and high pressure compressor. The control approach is the feedforward filtered-x least-mean-square algorithm implemented on a digital signal processing board. Reference transducers mounted on the engine case provides blade passing and harmonics frequency information to the controller. Error information is provided by large area microphones placed in the acoustic far field. In order to minimize the error signal, the controller actuates loudspeakers mounted on the inlet to produce destructive interference. The sound pressure level of the fundamental tone of the fan was reduced using the three channel controller by up to 16 dB over a 60 deg angle about the engine axis. A single channel controller could produce reduction over a 30 deg angle. The experimental results show the control to be robust. Simultaneous control of two tones is done with parallel controllers. The fundamental and the first harmonic tones of the fan were controlled simultaneously with reductions of 12 dBA and 5 dBA, respectively, measured on the engine axis. Simultaneous control was also demonstrated for the fan fundamental and the high pressure compressor fundamental tones.

Thomas, Russell H.↗

NASA NTRS1993

An application of artificial neural networks to experimental data approximation

As an initial step in the evaluation of networks, a feedforward architecture is trained to approximate experimental data by the backpropagation algorithm. Several drawbacks were detected and an alternative learning algorithm was then developed to partially address the drawbacks. This noniterative algorithm has a number of advantages over the backpropagation method and is easily implemented on existing hardware.

Meade, Andrew J., Jr.↗

NASA NTRS1992

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

Cios, Krzysztof J.↗

NASA NTRS1992

Direct model reference adaptive control with application to flexible robots

A modification to a direct command generator tracker-based model reference adaptive control (MRAC) system is suggested in this paper. This modification incorporates a feedforward into the reference model's output as well as the plant's output. Its purpose is to eliminate the bounded model following error present in steady state when previous MRAC systems were used. The algorithm was evaluated using the dynamics for a single-link flexible-joint arm. The results of these simulations show a response with zero steady state model following error. These results encourage further use of MRAC for various types of nonlinear plants.

Steinvorth, Rodrigo↗

NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗

NASA NTRS1992

Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem

The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design.

Troudet, T.↗

NASA NTRS1992

Neural network structure for navigation using potential fields

A hybrid-network method for obstacle avoidance in the truck-backing system of D. Nguyen and B. Widrow (1989) is presented. A neural network technique for vehicle navigation and control in the presence of obstacles has been developed. A potential function which peaks at the surface of obstacles and has its minimum at the proper vehicle destination is computed using a network structure. The field is guaranteed not to have spurious local minima and does not have the property of flattening-out far from the goal. A feedforward neural network is used to control the steering of the vehicle using local field information. The network is trained in an obstacle-free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.

Plumer, Edward S.↗

NASA NTRS1992

Classification of remotely sensed data using OCR-inspired neural network techniques

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Kiang, Richard K.↗

NASA NTRS1992

Robust manipulator controller specification and design

This paper suggests the use of a model based control law which uses joint rate feedback to stabilize the contact modes, and joint velocity feedforward to provide the required freespace performance throughout a task. An additional benefit of this controller is the capability to stiffen a soft manipulator. This provides an option for impedance control to provide any desired manipulator stiffness. Impedance control is implemented as an outer Cartesian loop which uses the dynamic relationship between manipulator commanded position and applied force to change the mechanical impedance through programmable filter coefficients. The impedance control discussed in this paper does not give the capability to specify the full impedance of the manipulator. However, it does give the capability to specify the manipulator Cartesian stiffness while maintaining stability in contact with the environment. This controller was designed for the Flight Telerobotic Servicer dextrous manipulator and has been validated experimentally on a single joint controller, as well as on 3 DOF planar arms.

Goldenberg, Stewart↗

NASA NTRS1993

Application of neural nets in structural optimization

The biological motivation for Artificial Neural Net developments is briefly discussed, and the most popular paradigm, the feedforward supervised learning net with error back propagation training algorithm, is introduced. Possible approaches for utilization in structural optimization is illustrated through simple examples. Other currently ongoing developments for application in structural mechanics are also mentioned.

Berke, Laszlo↗

NASA NTRS1991

A time domain inverse dynamic method for the end point tracking control of a flexible manipulator

The inverse dynamic equation of a flexible manipulator was solved in the time domain. By dividing the inverse system equation into the causal part and the anticausal part, we calculated the torque and the trajectories of all state variables for a given end point trajectory. The interpretation of this method in the frequency domain was explained in detail using the two-sided Laplace transform and the convolution integral. The open loop control of the inverse dynamic method shows an excellent result in simulation. For real applications, a practical control strategy is proposed by adding a feedback tracking control loop to the inverse dynamic feedforward control, and its good experimental performance is presented.

Kwon, Dong-Soo↗

NASA NTRS1993

Microgravity vibration isolation technology: Development to demonstration

The low gravity environment provided by space flight has afforded the science community a unique area for the study of fundamental and technological sciences. However, the dynamic environment observed on space shuttle flights and predicted for Space Station Freedom has complicated the analysis of prior 'microgravity' experiments and prompted concern for the viability of proposed space experiments requiring long term, low gravity environments. Thus, isolation systems capable of providing significant improvements to this random environment have been developed. This dissertation deals with the design constraints imposed by acceleration sensitive, microgravity experiment payloads in the unique environment of space. A theoretical background for the inertial feedback and feedforward isolation of a payload was developed giving the basis for two experimental active inertial isolation systems developed for the demonstration of these advanced active isolation techniques. A prototype six degree of freedom digital active isolation system was designed and developed for the ground based testing of an actively isolated payload in three horizontal degrees of freedom. A second functionally equivalent system was built for the multi-dimensional testing of an active inertial isolation system in a reduced gravity environment during low gravity aircraft trajectories. These multi-input multi-output control systems are discussed in detail with estimates on acceleration noise floor performance as well as the actual performance acceleration data. The attenuation performance is also given for both systems demonstrating the advantages between inertial and non-inertial control of a payload for both the ground base environment and the low gravity aircraft acceleration environment. A future goal for this area of research is to validate the technical approaches developed to the 0.01 Hz regime by demonstrating a functional active inertial feedforward/feedback isolation system during orbital flight. A NASA IN-STEP flight experiment has been proposed to accomplish this goal, and the expected selection for the IN-STEP program has been set for Jul. of 1993.

Grodsinsky, Carlos M.↗

NASA NTRS1993

Intervention strategies for the management of human error

This report examines the management of human error in the cockpit. The principles probably apply as well to other applications in the aviation realm (e.g. air traffic control, dispatch, weather, etc.) as well as other high-risk systems outside of aviation (e.g. shipping, high-technology medical procedures, military operations, nuclear power production). Management of human error is distinguished from error prevention. It is a more encompassing term, which includes not only the prevention of error, but also a means of disallowing an error, once made, from adversely affecting system output. Such techniques include: traditional human factors engineering, improvement of feedback and feedforward of information from system to crew, 'error-evident' displays which make erroneous input more obvious to the crew, trapping of errors within a system, goal-sharing between humans and machines (also called 'intent-driven' systems), paperwork management, and behaviorally based approaches, including procedures, standardization, checklist design, training, cockpit resource management, etc. Fifteen guidelines for the design and implementation of intervention strategies are included.

Wiener, Earl L.↗

NASA NTRS1993

Broadband linearisation of high-efficiency power amplifiers

A feedforward-based amplifier linearization technique is presented which is capable of yielding significant improvements in both linearity and power efficiency over conventional amplifier classes (e.g. class-A or class-AB). Theoretical and practical results are presented showing that class-C stages may be used for both the main and error amplifiers yielding practical efficiencies well in excess of 30 percent, with theoretical efficiencies of much greater than 40 percent being possible. The levels of linearity which may be achieved are required for most satellite systems, however if greater linearity is required, the technique may be used in addition to conventional pre-distortion techniques.

Kenington, Peter B.↗