This article presents a computed-torque controller plus adaptive fuzzy trajectory feedforward compensator suitable for the trajectory tracking control of uncertain underwater vehicle. To address the issue of unavailable normalization factor, an adaptive fuzzy trajectory feedforward compensator is proposed and assembled at the input trajectory level of the computed-torque controller rather than at the joint drive torque position. The compensator serving as a low-pass filter is implemented outside the inner control loop by adjusting the desired characteristic depth. Due to the nearly unchanged internal control algorithm, the adaptive fuzzy compensator is feasible to implement and is robust when varying the feedback gain in the inner control loop. Moreover, an adaptive dead zone fuzzy compensator is designed to reduce the effect of the dead zone on the actuators of underwater vehicles according to the unknown input dead zone characteristics. To validate the effectiveness of the proposed controller, simulations are conducted for a desired characteristic depth, and the performance of the proposed controller has been compared with conventional controllers to illustrate the usefulness and efficiency of the proposed controller.
An adaptive fuzzy sliding mode controller is proposed for the depth control of an underactuated underwater vehicle based on the state-dependent Riccati equation. An adaptive fuzzy control algorithm is embedded into the sliding mode controller to solve the buffering and mismatched uncertain problems in the robust sliding mode variable structure controller, where an auxiliary fuzzy control unit is designed to automatically adjust the scale factor of the main fuzzy controller output. Based on Lyapunov stability theory and final value bounded theorem, the stability and convergence properties of the closed-loop system are demonstrated. Numerical simulations are carried out to validate the effectiveness of the proposed controller.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.