  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.
