Tracking control of underwater vehicle subject to uncertainties using fuzzy inverse desired trajectory compensation technique

Computed-torque controller plus fuzzy inverse desired trajectory compensation technique based on robust adaptive fuzzy observer is proposed to control underwater vehicle subject to uncertainties. A fuzzy inverse desired trajectory compensator is developed as a nonlinear filter at input trajectory level outside the control loop to address the issue of unavailable normalizing factor. A robust adaptive state observer with loose constraint on the position of the uncertainty function is proposed to evaluate the unavailable states. Numerical simulation results of regulation performance demonstrate that the observer solves the problem of strict constraint conditions on position uncertainties. Comparisons of tracking performance between the proposed control method and computed-torque controller are performed. The results confirm that compensation at the input trajectory offers better position tracking performance and easier practical implementation than other fuzzy compensation techniques at joint torque level. The proposed control approach is simulated and its efficiency is validated through the simulation of an underwater vehicle.

[1]  Li-Xin Wang.  Stable adaptive fuzzy control of nonlinear systems , 1993, IEEE Trans. Fuzzy Syst..

[2]  Jun Gao,et al.  Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics: , 2012 .

[3]  Jianqiang Yi,et al.  A computed torque controller for uncertain robotic manipulator systems: Fuzzy approach , 2005, Fuzzy Sets Syst..

[4]  Tieshan Li,et al.  A DSC and MLP based robust adaptive NN tracking control for underwater vehicle , 2013, Neurocomputing.

[5]  Shaocheng Tong,et al.  Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems with dynamical uncertainties , 2011, Nonlinear Dynamics.

[6]  Junku Yuh,et al.  Design and Control of Autonomous Underwater Robots: A Survey , 2000, Auton. Robots.

[7]  Mingjun Zhang,et al.  Adaptive sliding mode control based on local recurrent neural networks for underwater robot , 2012 .

[8]  Miomir Vukobratović,et al.  A Bio-Inspired Approach to the Realization of Sustained Humanoid Motion , 2012 .

[9]  Abdesselem Boulkroune,et al.  Indirect adaptive fuzzy control scheme based on observer for nonlinear systems: A novel SPR-filter approach , 2014, Neurocomputing.

[10]  Ian Postlethwaite,et al.  A Simple Robust Control Scheme for Robot Manipulators With Only Joint Position Measurements , 1993, Int. J. Robotics Res..

[11]  Abdesselem Boulkroune,et al.  Design of a unified adaptive fuzzy observer for uncertain nonlinear systems , 2014, Inf. Sci..

[12]  Frank L. Lewis,et al.  Robot Manipulator Control: Theory and Practice , 2003 .

[13]  Yijun Du,et al.  Indirect adaptive fuzzy observer and controller design based on interval type-2 T–S fuzzy model , 2012 .

[14]  Mohammad Jafar Sadigh,et al.  A nonlinear high gain observer based input–output control of flexible link manipulator , 2012 .

[15]  Abdesselem Boulkroune,et al.  On the design of observer-based fuzzy adaptive controller for nonlinear systems with unknown control gain sign , 2012, Fuzzy Sets Syst..

[16]  Mohd Rizal Arshad,et al.  A hybrid-driven underwater glider model, hydrodynamics estimation, and an analysis of the motion control , 2014 .

[17]  Petros A. Ioannou,et al.  Robust Adaptive Control , 2012 .

[18]  Thor I. Fossen,et al.  Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles , 2002 .

[19]  Yu-Hsien Lin,et al.  Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles , 2015 .

[20]  Liang Jie.  FUZZY LOGIC ADAPTIVE COMPENSATION CONTROL FOR DUAL-ARM SPACE ROBOT BASED ON COMPUTED TORQUE CONTROLLER TO TRACK DESIRED TRAJECTORY IN INERTIA SPACE , 2010 .

[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]  Wei Zeng,et al.  Learning from NN output feedback control of robot manipulators , 2014, Neurocomputing.

[23]  Wen-Shyong Yu,et al.  Robust tracking observer-based adaptive fuzzy control design for uncertain nonlinear MIMO systems with time delayed states , 2015, Inf. Sci..

[24]  Marios M. Polycarpou,et al.  A Robust Adaptive Nonlinear Control Design , 1993, 1993 American Control Conference.

[25]  Yogesh Singh,et al.  Indirect disturbance compensation control of a planar parallel (2-PRP and 1-PPR) robotic manipulator , 2014 .

[26]  B. Imani,et al.  Modeling, path planning and control of a planar five-link bipedal robot by an adaptive fuzzy computed torque controller (AFCTC) , 2013, 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[27]  Hee-Jun Kang,et al.  An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator , 2013, Neurocomputing.

[28]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[29]  Peng Li,et al.  Adaptive Fuzzy Control of Stewart Platform under Actuator Saturation , 2009 .