Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters

Neural network based architectures for aerospace applications

A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Self-organized learning in multi-layer networks

We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learnin

Design and simulation of advanced fault tolerant flight control schemes

This research effort describes the design and simulation of a distributed Neural Network (NN) based fault tolerant flight control scheme and the interface of the scheme within a simulation/visualization environment. The goal of the fault tolerant flight control scheme is to recover an aircraft from failures to its sensors or actuators. A commercially available simulation package, Aviator Visual Design Simulator (AVDS), was used for the purpose of simulation and visualization of the aircraft dynamics and the performance of the control schemes.;For the purpose of the sensor failure detection, identification and accommodation (SFDIA) task, it is assumed that the pitch, roll and yaw rate gyros onboard are without physical redundancy. The task is accomplished through the use of a Main Neural Network (MNN) and a set of three De-Centralized Neural Networks (DNNs), providing analytical redundancy for the pitch, roll and yaw gyros. The purpose of the MNN is to detect a sensor failure while the purpose of the DNNs is to identify the failed sensor and then to provide failure accommodation. The actuator failure detection, identification and accommodation (AFDIA) scheme also features the MNN, for detection of actuator failures, along with three Neural Network Controllers (NNCs) for providing the compensating control surface deflections to neutralize the failure induced pitching, rolling and yawing moments. All NNs continue to train on-line, in addition to an offline trained baseline network structure, using the Extended Back-Propagation Algorithm (EBPA), with the flight data provided by the AVDS simulation package.;The above mentioned adaptive flight control schemes have been traditionally implemented sequentially on a single computer. This research addresses the implementation of these fault tolerant flight control schemes on parallel and distributed computer architectures, using Berkeley Software Distribution (BSD) sockets and Message Passing Interface (MPI) for inter-process communication

Self-repairing mobile robotic car using astrocyte-neuron networks

A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application