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

In recent times, with the incremental demand for fully autonomous systems, research interests are observed in learning machine-based intelligent, self-organizing, and evolving controllers. In this paper, a new evolving and self-organizing controller, namely generic-controller (G-controller), is proposed. The G-controller works in a fully online mode with minor expert domain knowledge. It is developed by incorporating the sliding mode control (SMC) theory with an advanced incremental learning machine, namely generic evolving neuro-fuzzy inference system. The controller starts operating from scratch with an empty set of fuzzy rule, and therefore, no offline training is required. To cope with the changing dynamic characteristics of the plant, the controller can add or prune the rules on demand. Control law and adaptation laws for the consequent parameters are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of the G-controller is guaranteed by 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 nonaxis parallel data from the plant and consequently, enhances the robustness against uncertainties and environmental perturbations. Finally, the controller's 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 and hexacopter for a variety of trajectories.