Adaptive fuzzy sliding mode formation controller for autonomous underwater vehicles with variable payload

Abstract

PurposeIn this paper, an adaptive fuzzy sliding mode controller (AFSMC) is developed for the formation control of a team of autonomous underwater vehicles (AUVs) subjected to unknown payload mass variations during their mission.Design/methodology/approachA sliding mode controller (SMC) is designed to drive the state trajectories of the AUVs to a switching surface in the state space. The payload mass variation results in parameter variation in AUV dynamics leading to actuator failure. This further leads to loss of communication among the members of the team. Hence, an adaptive SMC based on fuzzy logic is developed to maintain the coordinated motion of AUVs with payload mass variation.FindingsThe results are obtained by employing adaptive SMC for AUVs with and without payload variations and are compared. It is observed that the proposed adaptive SMC exhibits improved performance and tracks the desired trajectory in less time even with variation in the payload. The adaptive fuzzy control algorithm is developed to handle variation in payload mass variation. Lyapunov theory is used to establish stability of AFSMC controller.Research limitations/implicationsPerfect alignment is assumed between centres of gravity (OG) and buoyancy (OB), thus AUVs maintaining horizontal stability during motion. The AUVs’ body centres are aligned with centres of gravity (OG), thus the distance vector being rg = [0,0,0]T. As it is a tracking problem, sway motion cannot be neglected as the AUVs are travelling in a curved locus, hence susceptible to Coriolis and centripetal forces. The AUV is underactuated as only two thrusters at the stern plate that are employed for the surge and yaw controls and error in Y- direction are controlled by adjusting control input in surge and heave direction. Control inputs to the thruster are constants, and depth control is achieved by adjusting the rudder angle.Practical implicationsAUVs are employed in military mission or surveys, and they carry heavy weapons or instrument to be deployed at or picked from specific locations. Such tasks lead to variation in payload, causing overall mass variation during an AUV’s motion. A sudden change in the mass after an AUV release or pick load results in variation in depth and average velocity.Social implicationsThe proposed controller can be useful for military missions for carrying warfare and hydrographic surveys for deploying instruments.Originality/valueA proposed non-linear SMC has been designed, and its performances have been verified in terms of tracking error in X, Y and Z directions. An adaptive fuzzy SMC has been modelled using quantized state information to compensate payload variation. The stability of AFSMC controller is established by using Lyapunov theorem, and reachability of the sliding surface is ensured.

Hierarchical rule‐base reduction fuzzy control for constant velocity path tracking of a differential steer vehicle

“…This combination is accomplished by fuzzifying the output of sliding mode controllers to reduce chatter (Jae‐Oh et al, 2011; Song & Smith, 2000). An application of the fuzzy sliding mode controller can be seen in Hwang et al (2018) and Panda et al (2020) for an autonomous ground vehicle (AGV) and autonomous underwater vehicles (AUV), respectively, both with considerations for payload. Another advantage of fuzzy is its ability to simplify parameter adjustment when paired with other controllers, as in Wang et al (2020), this was achieved when paired with a sliding mode active disturbance rejection controller for an AUV.…”

Hierarchical Rule-Base Reduction Fuzzy Control for Constant Velocity Path Tracking of a Differential Steer Vehicle

“…This combination is accomplished by fuzzifying the output of sliding mode controllers to reduce chatter (Lee Jae-Oh, Han In-Woo, & Lee Jang-Myung, 2011) (Song & Smith, 2000). An application of the fuzzy sliding mode controller can be seen (Hwang, Yang, & Hung, 2018) and (Panda, Das, Subudhi, & Pati, 2020) for an Autonomous Ground Vehicle (AGV) and Autonomous Underwater Vehicles (AUV) respectively, both with considerations for payload. Another advantage of fuzzy is its ability to simplify parameter adjustment when paired with other controllers, as in (H. Wang, Li, Liu, Karkoub, & Zhou, 2020), this was achieved when paired with a sliding mode active disturbance rejection controller for an AUV.…”