Abstract
Robotic aircraft are often required to operate in harsh environments (e.g., underground mining, cluttered environments, and battlefields). In this chapter, we discuss an adaptive (evolving) fuzzy system that has the ability to learn and to configure itself based on the human way of learning, which is also somewhat akin to the principles of natural evolution. We will be looking at the capability of an evolving Takagi-Sugeno (ETS) fuzzy algorithm to learn-from-scratch in order to adapt the challenging dynamics of autonomous systems in real-time. The ETS system can also work in unknown environments, where there is no expert knowledge. While we focus on the implementation of the ETS system to identify the behavior of a fast-dynamical system as in the case of the low altitude hovering of our Tarot hexacopter drone by performing an online ETS-based data driven modelling (online system identification) technique, we also conduct a preliminary study to highlight the efficacy of the ETS autopilot under computer simulations.
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