Neural Network Applications in Advanced Aircraft Flight Control System, a Hybrid System, a Flight Test Demonstration

Abstract

Modern exploration missions require modern control systems that can handle catastrophic changes in behavior, compensate for slow deterioration in sustained operations, and support fast system identification. The dynamics and control of new vehicles remains a significant technical challenge. Neural network based adaptive controllers have these capabilities, but they can only be used safely if proper Verification and Validation can be done. Due to the nonlinear and dynamic nature of an adaptive control system, traditional Verification and Validation (V&V) and certification techniques are not sufficient for adaptive controllers, which is a big barrier in their deployment in the safety-critical applications. Moreover, traditional methods of V&V involve testing under various conditions which is costly to run and requires scheduling a long time in advance. We have developed specific techniques, tools, and processes to perform design time analysis, verification and validation, and dynamic monitoring of such controllers. Combined with advanced modelling tools, an integrated development or deployment methodology for addressing complex control needs in a safety- and reliability-critical mission environment can be provided.