Design of a sliding mode fuzzy controller for the guidance and control of an autonomous underwater vehicle
Introduction
Control problems involving autonomous underwater vehicles (AUVs) present several difficulties, owing to their non-linear dynamics, the presence of disturbance, and observation noises. Ocean exploration and the utilization of oceanic resources in shallow, confined water areas have received increasing interest in recent years. In such regions, shallow water phenomena from the interaction among wave dynamics, tidal currents, coastal currents, and artificial objects create a complex environment for operating unmanned underwater vehicles. Therefore, controlling AUVs to satisfactorily track trajectories in shallow waters remains a challenge.
Several control strategies have been developed for controlling the motion of underwater vehicles, among them are, supervisory control (Yoerger et al., 1986), neural network control (Yuh, 1990), self-turning control (Goheen and Jefferys, 1990), LQG/LTR (Triantafyllou and Grosenbaugh, 1991), sliding mode control (Yoerger and Slotine, 1985; Fossen and Sagatun, 1991; da Cunha et al., 1995; Christi et al., 1990; Healey and Lienard, 1993; Lam and Ura, 1996; Lee et al., 1999), fuzzy logic control (Kato et al., 1993, Smith et al., 1994), and recently, the sliding mode fuzzy logic control (Song and Smith, 2000). More references could be found from the underwater robotics community, for example (Yuh, 1994, Yuh et al., 1996). This paper presents a design method based on sliding mode fuzzy logic control. As well recognized, fuzzy logic controllers are effective robust controllers for various applications. A merit of using fuzzy logic to design a controller is that the dynamics of the controlled system need not be fully known. However, the linguistic expression of the fuzzy controller makes it difficult to guarantee the stability and robustness of the control system. Sliding mode control can also be applied effectively in the presence of model uncertainties, parameter variations, and disturbances. A boundary layer is generally used to avoid chattering on the sliding surface (Slotine and Li, 1991). Designing a fuzzy logic controller based on the sliding mode theory assures performance and stability, while simultaneously reducing the number of fuzzy rules. Furthermore, fuzzy partition of the manipulated variables avoids the chattering problem of the sliding mode control method (Palm, 1994, Palm et al., 1996). The ‘sliding mode fuzzy logic controller (SMFLC)’ is thus adopted herein as the basic controller structure.
Section snippets
Background
In Yoerger and Slotine (1985), sliding mode control methodology is applied to control trajectories of remotely operated vehicles (ROVs). It is demonstrated that a sliding mode controller can be designed using a simple nonlinear model of the vehicle. The controller effectively deals with nonlinearities, and is robust even with imprecise models. Furthermore, the trade-off between performance and model uncertainty is predictable. In Fossen and Sagatun (1991), a hybrid controller combining an
Guidance law
A one-degree-of-freedom vehicle model is used herein to describe the horizontal turning behavior of the AUV. The model includes drag, added mass, and thrust moment for yaw motion, where I denotes the vehicle’s mass moment of inertia plus the added inertia of the body about the body-fixed z-axis, r represents the body-fixed rate for heading direction, b denotes the square-law damping coefficient, u is the moment generated by commanding differential thrust force on the left and
Controller design
A sliding mode formulation is used to design controller parameters. The control law u(t) is designed so that the system trajectories are ultimately bounded under the region B={SE,|SE≤ Φ} ; Φ>0. This goal can be achieved by satisfying the following sliding condition outside region B (Slotine and Li, 1991),where the sliding error SE is defined in Eq. (5). The constant η is a design parameter that is inversely proportional to the time required for the SE to reach the boundary of
Experiments
Tank tests and open sea trials were conducted to determine the effects of the design parameters. The testing tank was 120×8×4 m (L×W×D). Meanwhile, the testbed vehicle AUV-HM1 had dimensions of 2×1×0.6 m (L×W×D), as displayed in Fig. 7. The vehicle employed a 300 kHz Doppler velocity log and a fiber-optics gyro as navigation sensors. The sea trial area had a water depth of 60 to 80 m. To test the effect of surface currents and waves on the effectiveness of the control algorithm, the vehicle was
Conclusions
This study has demonstrated the feasibility of applying a sliding mode fuzzy controller to an AUV in shallow water in order to perform line-of-sight guidance in the horizontal plane. The state dependent control gain is specified by a set of shrinking-span and dilating-span factors. Design parameters of this controller, such as the slope at the origin, and the stability and robustness bound, are specified using defined criteria. The effect of selecting different control parameters is evaluated
Acknowledgements
The authors would like to thank the National Science Council of ROC for financially supporting this research under Contract No. NSC88-2611-E002-020.
References (22)
- et al.
Systematic design and analysis of fuzzy-logic control and application to robotics, Part II. Control
Robotics and Autonomous Systems
(2000) Robust control by fuzzy sliding mode
Automatica
(1994)- et al.
Fuzzy logic control of an autonomous underwater vehicle
Control Engineering Practice
(1994) - et al.
User-friendly design method for fuzzy logic controller
IEE Proceedings–Control Theory and Applications
(1996) - et al.
On the linear hydrodynamic forces and the maneuverability of an unmanned untethered submersible with streamlined body
Journal of the Japanese Society of Naval Architecture
(1997) - et al.
Adaptive sliding mode control of autonomous underwater vehicles in the dive plane
IEEE Journal of Oceanic Engineering
(1990) - et al.
Design of a high performance variable structure control of ROV’s
IEEE Journal of Oceanic Engineering
(1995) - et al.
Adaptive control of nonlinear systems: A case study of underwater robotic systems
Journal of Robotic Systems
(1991) - et al.
Multivariable self-turning autopilots for autonomous underwater vehicles
IEEE Journal of Oceanic Engineering
(1990) - et al.
Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles
IEEE Journal of Oceanic Engineering
(1993)
Guidance and control of autonomous underwater vehicle AQUA EXPLORER 1000 for inspection of underwater cables
Cited by (125)
Composite learning adaptive sliding mode control for AUV target tracking
2019, NeurocomputingDouble-loop sliding mode controller with a novel switching term for the trajectory tracking of work-class ROVs
2019, Ocean EngineeringCitation Excerpt :In addition to the interference force of the umbilical, the ocean current force and the model uncertainties pose challenges to ROV automatic control. To solve these problems, many advanced control methods can be applied to ROV control such as adaptive control (Antonelli et al., 2003; Hoang and Kreuzer, 2007; Maalouf et al., 2015; Shojaei and Arefi, 2015), sliding mode control (Bessa et al., 2010; Elmokadem et al., 2016; Garcí;A-Valdovinos et al., 2009; Guo et al., 2003; M. Kim et al., 2015; Soylu et al., 2008), fuzzy logic control (Chen et al., 2016; Huo et al., 2018; Khodayari and Balochian, 2015), backstepping control (Do, 2015; Raygosa-Barahona et al., 2011; Wang et al., 2015; Zhu and Gu, 2011), and neural network control (Chu et al., 2017; Gao et al., 2015; Kim and Kim, 2014). Sliding mode control (SMC) is characterized by a simple form, strong robustness, tolerance of model uncertainty and transient stability (Qiao et al., 2017).
Dual-Loop Integral Sliding Mode Control-Based Path Tracking with Reaction Torque for Autonomous Underwater Vehicle
2024, Journal of Marine Science and Engineering