Filter-backstepping based neural adaptive formation control of leader-following multiple AUVs in three dimensional space

Abstract This paper addresses a filter-backstepping based neural adaptive formation control of leader-following AUVs with model uncertainties and external disturbances in three dimensional space. For this purpose, a formation control strategy is proposed by employing filter backstepping technique to avoid the explosion of complexity in the standard backstepping design. This technique can not only significantly simplify the design process of the backstepping controller, but also reduce the influence of high-frequency measurement noise. Then, multi-layer neural networks are incorporated with an adaptive robust technique to deal with the uncertain dynamics, approximation errors and nonlinear disturbances induced by the waves and ocean currents. A Lyapunov-based stability analysis is provided to guarantee that the tracking errors for all the AUVs are uniformly ultimately bounded (UUB). Finally, simulation results for a group of AUVs are provided to demonstrate the tracking performance of the designed formation controller.

[1]  Marios M. Polycarpou,et al.  Command filtered backstepping , 2009, 2008 American Control Conference.

[2]  Domenico Prattichizzo,et al.  Discussion of paper by , 2003 .

[3]  Kouhei Ohnishi,et al.  Autonomous decentralized control for formation of multiple mobile robots considering ability of robot , 2004, IEEE Transactions on Industrial Electronics.

[4]  Xue Qi,et al.  Three-dimensional formation control based on filter backstepping method for multiple underactuated underwater vehicles , 2017, Robotica.

[5]  Hao Wang,et al.  Distributed cooperative stabilisation of continuous-time uncertain nonlinear multi-agent systems , 2014, Int. J. Syst. Sci..

[6]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[7]  Thor I. Fossen,et al.  Ship Formation Control: A Guided Leader-Follower Approach , 2008 .

[8]  Dan Wang,et al.  Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics , 2013, IEEE Transactions on Control Systems Technology.

[9]  K. D. Do Coordination control of underactuated ODINs in three-dimensional space , 2013, Robotics Auton. Syst..

[10]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[11]  Marios M. Polycarpou,et al.  Stable adaptive neural control scheme for nonlinear systems , 1996, IEEE Trans. Autom. Control..

[12]  Khoshnam Shojaei,et al.  Observer-based neural adaptive formation control of autonomous surface vessels with limited torque , 2016, Robotics Auton. Syst..

[13]  Muhammad Junaid Khan,et al.  Integral terminal sliding mode formation control of non-holonomic robots using leader follower approach , 2016, Robotica.

[14]  Zheping Yan,et al.  Discrete-time coordinated control of leader-following multiple AUVs under switching topologies and communication delays , 2019, Ocean Engineering.

[15]  Farbod Fahimi,et al.  Sliding-Mode Formation Control for Underactuated Surface Vessels , 2007, IEEE Transactions on Robotics.

[16]  Omid Elhaki,et al.  A robust neural network approximation-based prescribed performance output-feedback controller for autonomous underwater vehicles with actuators saturation , 2020, Eng. Appl. Artif. Intell..

[17]  Wei Meng,et al.  Nonlinear sliding mode formation control for underactuated surface vessels , 2012, WCICA 2012.

[18]  Yoo Sang Choo,et al.  Leader-follower formation control of underactuated autonomous underwater vehicles , 2010 .

[19]  Weidong Zhang,et al.  Leader-follower formation control of underactuated surface vehicles based on sliding mode control and parameter estimation. , 2017, ISA transactions.

[20]  Xue Qi,et al.  Three-dimensional formation control based on nonlinear small gain method for multiple underactuated underwater vehicles , 2018 .

[21]  Erfu Yang,et al.  Nonlinear Formation-Keeping and Mooring Control of Multiple Autonomous Underwater Vehicles , 2007, IEEE/ASME Transactions on Mechatronics.

[22]  João P. Hespanha,et al.  Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty , 2007, IEEE Transactions on Automatic Control.

[23]  Anthony J. Calise,et al.  Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks , 2002, IEEE Trans. Neural Networks.

[24]  Cong Wang,et al.  Command filter based adaptive neural trajectory tracking control of an underactuated underwater vehicle in three-dimensional space , 2019, Ocean Engineering.

[25]  K. D. Do,et al.  Formation control of underactuated ships with elliptical shape approximation and limited communication ranges , 2012, Autom..

[26]  Lu Liu,et al.  Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design. , 2015, ISA transactions.

[27]  Randal W. Beard,et al.  A coordination architecture for spacecraft formation control , 2001, IEEE Trans. Control. Syst. Technol..

[28]  Hao Wang,et al.  Distributed cooperative tracking of uncertain nonlinear multi-agent systems with fast learning , 2014, Neurocomputing.

[29]  Girish Chowdhary,et al.  Comparison of RBF and SHL Neural Network Based Adaptive Control , 2009, J. Intell. Robotic Syst..

[30]  Khoshnam Shojaei,et al.  Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque , 2015 .

[31]  Omid Elhaki,et al.  Neural network-based target tracking control of underactuated autonomous underwater vehicles with a prescribed performance , 2018, Ocean Engineering.

[32]  Khoshnam Shojaei,et al.  Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators , 2016, Neurocomputing.

[33]  Yudong Zhao,et al.  Lyapunov and Sliding Mode Based Leader-follower Formation Control for Multiple Mobile Robots with an Augmented Distance-angle Strategy , 2019, International Journal of Control, Automation and Systems.

[34]  Jun Wang,et al.  Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback , 2017, IEEE Transactions on Industrial Electronics.

[35]  Ji Xiang,et al.  Three-Dimensional Coordination Control for Multiple Autonomous Underwater Vehicles , 2019, IEEE Access.

[36]  Cong Wang,et al.  Sliding mode based neural adaptive formation control of underactuated AUVs with leader-follower strategy , 2020 .

[37]  Khoshnam Shojaei,et al.  Three-dimensional neural network tracking control of a moving target by underactuated autonomous underwater vehicles , 2019, Neural Computing and Applications.

[38]  Francisco R. Rubio,et al.  Formation Control of Autonomous Underwater Vehicles Subject to Communication Delays , 2014 .

[39]  Mansour A. Karkoub,et al.  Mixed Fuzzy Sliding-Mode Tracking with Backstepping Formation Control for Multi-Nonholonomic Mobile Robots Subject to Uncertainties , 2015, J. Intell. Robotic Syst..

[40]  Alireza Mohammad Shahri,et al.  Design and Implementation of an Inverse Dynamics Controller for Uncertain Nonholonomic Robotic Systems , 2013, J. Intell. Robotic Syst..

[41]  Chao Ma,et al.  Distributed formation control of 6-DOF autonomous underwater vehicles networked by sampled-data information under directed topology , 2015, Neurocomputing.