Adaptive Fuzzy Control Systems for Uncertainty-Aware Autonomous Navigation

Abstract

Autonomous vehicles operating on the ground, in the air, and underwater must track trajectories and avoid obstacles despite
unmodeled dynamics, drifting parameters, and imperfect sensing, conditions under which classical fixed-gain controllers
routinely degrade. This article synthesizes the literature on fuzzy control approaches to this problem, tracing a continuous line
from Zadeh's original fuzzy-set formalism and Mamdani and Assilian's first rule-based fuzzy controller through the
Takagi-Sugeno reformulation that made rigorous Lyapunov-based stability analysis tractable for nonlinear plants, to Wang's
foundational proof that a fuzzy system used as an online function approximator yields a stable, bounded-error adaptive
controller under substantial model uncertainty. We then review how this theoretical core, together with type-2 fuzzy logic's
explicit representation of meta-level uncertainty, has been built into applied navigation systems for mobile ground robots,
quadrotor UAVs, autonomous underwater and surface vehicles, steering-controlled ground vehicles, and robotic manipulators,
reporting the concrete quantitative gains -- and the more common qualitative stability guarantees -- these studies actually
document. Read together, the evidence supports adaptive and type-2 fuzzy control as a mature, repeatedly validated
architectural pattern for navigation under uncertainty, while leaving inconsistent performance-reporting standards, limited
field validation beyond simulation and short experimental windows, and the interpretability cost of combining fuzzy reasoning
with learning-based methods as an open research agenda.