Nonlinear trajectory tracking control of UVMS with compositing fuzzy disturbance observer and robust state-dependent Riccati equation

To improve the trajectory tracking accuracy of an underwater vehicle-manipulator system in the presence of external disturbances and model uncertainties, this work proposes a composite controller framework, integrating an adaptive fuzzy disturbance observer with robust state-dependent Riccati equation. The fuzzy disturbance observer utilizes a fuzzy logic system with rapid adaptive laws on-line to predict and assess external disturbances. In addition, the robust state-dependent Riccati equation controller optimizes tracking robustness by compensating for model uncertainties terms and deriving tuning rules for weighting matrices, preventing that a fixed gain of conventional state-dependent Riccati equation degrades tracking performance under model uncertainties. The stability of the proposed controller is verified through the theoretical analysis of Lyapunov. Finally, simulation cases demonstrate the effectiveness of the proposed controller framework by tracking a complex trajectory.