Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-inspired Unmanned Aerial Vehicles

TL;DR

This paper introduces a novel evolving self-organizing neuro-fuzzy controller, G-controller, for UAVs that learns online with minimal prior knowledge, ensuring stability and robustness in uncertain environments.

Contribution

It proposes the G-controller combining SMC theory with GENEFIS, enabling online learning, rule adaptation, and guaranteed stability for UAV control without offline training.

Findings

Effective tracking of UAV trajectories demonstrated

Controller adapts rules dynamically for robustness

Stable closed-loop control confirmed through Lyapunov analysis

Abstract

At recent times, with the incremental demand of the fully autonomous system, a huge research interest is observed in learning machine based intelligent, self-organizing, and evolving controller. In this work, a new evolving and self-organizing controller namely Generic-controller, G-controller, is proposed. The G-controller that works in the fully online mode with very minor expert domain knowledge is developed by incorporating the sliding model control, SMC, theory based learning algorithm with an advanced incremental learning machine namely Generic Evolving Neuro-Fuzzy Inference System , GENEFIS. The controller starts operating from scratch with an empty set of fuzzy rules, and therefore, no offline training is required. To cope with the plant vulnerable behavior, the controller can add, or prune the rules on demand. Control law and adaptation laws for the consequents are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of the G-controller is guaranteed using the Lyapunov function. The uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. In addition, the implementation of the multivariate Gaussian function helps the controller to handle the non-axis parallel data from the plant and consequently enhances the robustness against the uncertainties and environmental perturbations. Finally, the controller performance has been evaluated by observing the tracking performance in controlling simulated plants of unmanned aerial vehicle namely bio-inspired flapping wing micro air vehicle BIFW MAV and hexacopter for a variety of trajectories.