Adaptive fuzzy tracking control for underactuated surface vessels with unmodeled dynamics and input saturation. (August 2020)

• Record Type: Journal Article
• Authors: Deng, Yingjie; Zhang, Xianku; Im, Namkyun; Zhang, Guoqing; Zhang, Qiang
• Is Part Of: ISA transactions. Volume 103(2020)
• Page Start: 52
• Page End: 62
• Publication Date: 2020-08
• Subjects: USV -- Adaptive fuzzy control -- Unmodeled dynamics -- Input saturation -- Small-gain theorem; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.04.010
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper investigates the tracking control of the underactuated surface vessel (USV) with the off-diagonal inertial matrix, and under the influence of unmodeled dynamics as well as the constraint of input saturation. With the fuzzy logic systems (FLSs) accounting for the uncertainties, we present an adaptive fuzzy state-feedback control scheme with the minimum learning parameters (MLPs) of the FLSs. Based on the conventional USV model described in three degrees of freedom (DOF), an improved model is established at first, which involves the terms of dynamic disturbances generated by the unmodeled dynamics of the indecisive motions. Then, the off-diagonal inertial matrix is released by restructuring the kinematic loop of the improved model, and the backstepping approach is employed through the control design. To solve the underactuated problem, the tracking error in the sway motion is restructured by adding an adaptive compensating variable and further allocated to the actuated motions. The K ∞ functions are structured in the control laws to offset the dynamic disturbances. The Gauss error functions are employed to approximate the uncertainties of the input saturation, which are described by the continuous control inputs with the bounded multiplicative gains. Via the small-gain theorem, the resulting closed-loop system is proved to be ultimately bounded. Finally, a simulation example is carried out to validate the effectiveness of the developed scheme. Highlights: An improved 3-DOF model with unmodeled dynamics is established for the first time. The K ∞ functions are involved in the control laws to conform to the small-gain theorem. The Gauss error functions are employed to approximate the saturation constraints. The fuzzy logic system is employed to approximate the uncertainties with minimum learning parameters. The off-diagonal inertia matrix is released by coordinate transformation. The underactuated problem is solved by adding an adaptive compensating variable to the sway tracking error.