Disturbance Observer-Based Fuzzy Adapted S-Surface Controller for Spatial Trajectory Tracking of Autonomous Underwater Vehicle

A new adaptive robust control scheme combining the disturbance-observer-based control (DOBC) with fuzzy adapted S-Surface control is proposed for trajectory tracking control of autonomous underwater vehicle. The main contribution of the proposed method is that control configuration does not require the bounds of uncertainty of the vehicle to be known and disturbances effect can be estimated and rejected. The proposed control law is mainly composed of three parts: a feed forward control along with disturbance estimator, S-Surface control and single input adaptive fuzzy proportional-integral (PI) compensator. The nominal feed forward controller specifies desired closed loop dynamics with extravagance from known preferred acceleration vector. Meanwhile, the disturbance observer and adaptive fuzzy PI control term to compensate the unknown effects that are disturbances and unmodeled dynamics. An additional term as S-Surface control assures fast convergence due to a nonlinear expression in to surface and also enhance stability of the underwater vehicle in oceanic environment. Moreover, the disturbance observer enhances the robustness performance of the adaptive fuzzy system for disturbances that cannot be modeled by fuzzy logic. The stability of closed loop controlled system is proven to be guaranteed according to Lyapunov theory. Finally, numerical simulation results illustrate the effectiveness and robustness of the proposed control method.

[1]  Byung Kook Kim,et al.  Design and stability analysis of single-input fuzzy logic controller , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Carlos Silvestre,et al.  On the design of gain-scheduled trajectory tracking controllers , 2002 .

[3]  Balasaheb M. Patre,et al.  Task Space Control of an Autonomous Underwater Vehicle Manipulator System by Robust Single-Input Fuzzy Logic Control Scheme , 2017, IEEE Journal of Oceanic Engineering.

[4]  Pedro Silva,et al.  A multi-objective genetic algorithm applied to autonomous underwater vehicles for sewage outfall plume dispersion observations , 2010, Appl. Soft Comput..

[5]  Zhengping Feng,et al.  Reduced Order H∞ Control of an Autonomous Underwater Vehicle , 2003 .

[6]  Junku Yuh,et al.  A neural net controller for underwater robotic vehicles , 1990 .

[7]  Michael S. Triantafyllou,et al.  Robust control for underwater vehicle systems with time delays , 1991 .

[8]  Jean-Jacques E. Slotine,et al.  Robust trajectory control of underwater vehicles , 1985 .

[9]  Shrivijay B. Phadke,et al.  Control of Uncertain Nonlinear Systems Using an Uncertainty and Disturbance Estimator , 2012 .

[10]  Mahdi Tavakoli,et al.  Nonlinear Disturbance Observer Design For Robotic Manipulators , 2013 .

[11]  Daqi Zhu,et al.  The bio-inspired model based hybrid sliding-mode tracking control for unmanned underwater vehicles , 2013, Eng. Appl. Artif. Intell..

[12]  Ming Gao,et al.  Multivariable Output Feedback Adaptive Terminal Sliding Mode Control for Underwater Vehicles , 2016 .

[13]  Cheruvu Siva Kumar,et al.  Robust trajectory control of underwater vehicles using time delay control law , 2007 .

[14]  Lei Zhang,et al.  Expert S-surface control for autonomous underwater vehicles , 2008 .

[15]  Thor I. Fossen,et al.  Guidance and control of ocean vehicles , 1994 .

[16]  Leila Notash,et al.  Adaptive sliding mode control with uncertainty estimator for robot manipulators , 2010 .

[17]  Mohan Santhakumar,et al.  Investigations on the Hybrid Tracking Control of an Underactuated Autonomous Underwater Robot , 2010, Adv. Robotics.

[18]  K. R. Goheen,et al.  Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles , 1990 .

[19]  Jian Xu,et al.  Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles , 2015 .

[20]  Alessandro Pisano,et al.  Output-feedback control of an underwater vehicle prototype by higher-order sliding modes , 2004, Autom..

[21]  Behdad Geranmehr,et al.  Nonlinear suboptimal control of fully coupled non-affine six-DOF autonomous underwater vehicle using the state-dependent Riccati equation , 2015 .

[22]  Mario Innocenti,et al.  Robust control of underwater vehicles: sliding mode control vs. mu synthesis , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[23]  A. Levant.  Robust exact differentiation via sliding mode technique , 1998 .

[24]  Jinwhan Kim,et al.  Integral sliding mode controller for precise manoeuvring of autonomous underwater vehicle in the presence of unknown environmental disturbances , 2015, Int. J. Control.

[25]  Jeen-Shing Wang,et al.  Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater vehicle , 2002, IEEE Trans. Robotics Autom..

[26]  Ji-Hong Li,et al.  Design of an adaptive nonlinear controller for depth control of an autonomous underwater vehicle , 2005 .

[27]  Sahjendra N. Singh,et al.  State-dependent Riccati equation-based robust dive plane control of AUV with control constraints , 2007 .

[28]  P.A. DeBitetto.  Fuzzy logic for depth control of unmanned undersea vehicles , 1994, Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94).

[29]  Laxman M. Waghmare,et al.  Robust maneuvering of autonomous underwater vehicle: an adaptive fuzzy PI sliding mode control , 2017, Intell. Serv. Robotics.

[30]  B. Jalving,et al.  The NDRE-AUV flight control system , 1994 .

[31]  Do Wan Kim,et al.  Tracking of REMUS autonomous underwater vehicles with actuator saturations , 2015, Autom..