Adaptive Flight Control to Enhance Survivability and Reduce Cost of Change

A number of applications of AI learning to control system design have been reported. These studies have been focusing on the controller design for the systems that have relatively simple dynamic characteristics such as car, robot manipulator, or pendulum. However, it is well-known that the dynamics characteristics of aircraft or UAS are relatively complicated: high nonlinear, rapidly changing dynamics, and uncertainties of aerodynamic parameters. Thus, the fundamental research question of this PhD programme is how to select an appropriate architecture of AI learning in order to find feasible and safe solution for such a complicated system. Another challenge is the validation of solution. The autopilot based on AI learning is generally considered as a "block-box" approach. The main problem of such a kind of approach is that no one can guarantee whether or not it will not make any issues when implementing it in a real system. However, previous studies have mostly focused on showing the feasibility only, but there has been lack of effort to validate such a block-box approach. Namely, understandings of the behaviour of black-box and the convergence of solution have been less understood. In practice, these are important in ensuring confidence in the performance and reliability of learning-based approach when implementing the autopilot based on learning-based approach in a real system.This research seeks an appropriate AI learning architecture for controlling of complicated dynamics systems such as aircraft or UAS. The innovation proposed will investigate a way to understand the behaviour of autopilot based on learning approach. The principle contribution will be a practical autopilot algorithm based on learning approach which is directly applicable to real systems in ensuring confidence in the reliability. The scientific value and innovation thus lie in not only development of a novel and a practical and reusable autopilot algorithm for aircraft or UAS, but also validation of the proposed algorithm based on theoretical analyses, numerical simulation, and flight tests. The primary aim of this project is to develop a practical and safe autopilot algorithm based on AI learning approach that can easily reconfigure its control algorithm according to the model changes. The overall approach is a combination of theoretical innovation with numerical simulations or flight test. Therefore, the specific objectives will include: a. Review state-of-the-art learning approaches and investigate their applicability to the flight control system; b. Develop realistic flight models; c. Develop a flight control system based on the most promising state-of-art AI learning approaches; d. Investigate an appropriate architecture of the adaptive flight control system developed; e. Identify relevant performance metrics for the adaptive flight control system; f. Investigate appropriate methods for the validation and verification of adaptive flight control systems; g. Analyse and validate the designed flight control system using the flight test-bed and performance metrics developed.