A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System

TL;DR

This paper introduces a self-evolving neuro-fuzzy controller for hexacopter altitude control, capable of learning from scratch without offline training, ensuring stability and high performance in complex nonlinear UAV dynamics.

Contribution

It proposes a novel generic evolving neuro-fuzzy controller based on GENEFIS, with online rule adaptation and stability guarantees for UAV altitude control.

Findings

Effective altitude tracking for various trajectories

No offline training required, starts with empty rules

Stable and robust control performance

Abstract

Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics, a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (G-controller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent parameters are derived from the sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain a uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of a UAV namely hexacopter for various trajectories.