this paper, we focus on methods of fault tolerance, and investigate the differences between hardware fault tolerance and software fault tolerance. 1.2 Fault, Error and Failure

As aircraft designs become more complex, automationhas become an important factor in improving safety and reliability. Automated flight control systems can respond intelligently to faults when it is impractical for a human to take control quickly. In recent years neural networks have been proposed for fault identification and accommodation purposes within flight control schemes because they are well suited to non-linear, multi-variable systems. Because neural networks learn to associate various control actions with particular input data patterns, they avoid the need to explicitly program all the relevant fault situations. A major

This report describes the ARGOS project at Stanford CRC. The primary goals of this project are to collect data on the errors that occur in digital integrated circuits in a space environment, to determine the tradeoffs between fault-avoidance and fault-tolerance, and to see if radiation hardening can be avoided by using fault tolerance techniques. Our experiments will be carried out on two processor boards on the ARGOS experimental satellite. One of the boards uses radiation-hardened components while the other uses only commercial off-the-shelf (COTS) parts. Programs and data can be uploaded to the boards during the mission. This capability allows us to evaluate different software fault-tolerance techniques. This report reviews various error detection techniques. Software techniques that do not require any special hardware are discussed. The framework of the software that we are developing for error data collection is presented. Key Words and Phrases: ARGOS, fault tolerance, control flow error detection,