Distributed guidance-based formation control of marine vehicles under switching topology

Abstract Because of the limited and unreliable communication topology in complex marine environment, the formation control of marine vehicles under switching topology is investigated in this paper, whereas the most previous works for marine formation focused on the fixed communication topology. In order to simplify the formation control design, a novel distributed guidance system, rather than the traditional distributed control system, is established based on leader-follower strategy only using a portion of communication links. To address the problem of input saturation of marine vehicle, the model predictive control is therefore employed to design the marine formation controller; furthermore, the predictive mechanism allows model predictive control to prevent the constraint violation in advance, which brings advantages against the large inertia of ship motion. Both formation tracking control and formation regulation control are studied herein. For application of formation tracking control of marine vehicles, i.e., the time-varying reference signal, the formation control can be realized under switching topology with the condition that the communication topology contains a directed spanning tree. For application of formation regulation control of marine vehicles, i.e., the constant reference signal, the formation control can be realized under switching topology with a relative milder condition that the union of communication topology contains a directed spanning tree. Multiple simulations are included to show the effectiveness of the presented distributed guidance-based MPC formation controllers.

[1]  Jun Zhang,et al.  Robust model predictive control for path-following of underactuated surface vessels with roll constraints , 2017 .

[2]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[3]  Wei Ren,et al.  Consensus Tracking Under Directed Interaction Topologies: Algorithms and Experiments , 2008, IEEE Transactions on Control Systems Technology.

[4]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[5]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[6]  Gang Wang,et al.  Model predictive controller design for ship dynamic positioning system based on state-space equations , 2017 .

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

[8]  Xiang Yu,et al.  Formation control and coordination of multiple unmanned ground vehicles in normal and faulty situations: A review , 2020, Annu. Rev. Control..

[9]  Hai Lin,et al.  Platoon Formation Control With Prescribed Performance Guarantees for USVs , 2018, IEEE Transactions on Industrial Electronics.

[10]  K. D. Do,et al.  Synchronization Motion Tracking Control of Multiple Underactuated Ships With Collision Avoidance , 2016, IEEE Transactions on Industrial Electronics.

[11]  Jian Li,et al.  Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation , 2019, Ocean Engineering.

[12]  Fei Luo,et al.  Leader–Follower Formation Control of USVs With Prescribed Performance and Collision Avoidance , 2019, IEEE Transactions on Industrial Informatics.

[13]  Hyo-Sung Ahn,et al.  A survey of multi-agent formation control , 2015, Autom..

[14]  T.I. Fossen,et al.  Formation Control of Marine Surface Craft: A Lagrangian Approach , 2006, IEEE Journal of Oceanic Engineering.

[15]  Ilya V. Kolmanovsky,et al.  A stable block model predictive control with variable implementation horizon , 2005, Proceedings of the 2005, American Control Conference, 2005..

[16]  Xu Jin,et al.  Nonrepetitive Leader–Follower Formation Tracking for Multiagent Systems With LOS Range and Angle Constraints Using Iterative Learning Control , 2019, IEEE Transactions on Cybernetics.

[17]  Guanghui Wen,et al.  Distributed Tracking of Nonlinear Multiagent Systems Under Directed Switching Topology: An Observer-Based Protocol , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[19]  Xu Jin,et al.  Fault tolerant finite-time leader-follower formation control for autonomous surface vessels with LOS range and angle constraints , 2016, Autom..

[20]  Tieshan Li,et al.  Modular Adaptive Control for LOS-Based Cooperative Path Maneuvering of Multiple Underactuated Autonomous Surface Vehicles , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[21]  Asgeir J. Sørensen,et al.  Identification of Dynamically Positioned Ships , 1995 .

[22]  Randal W. Beard,et al.  Consensus seeking in multiagent systems under dynamically changing interaction topologies , 2005, IEEE Transactions on Automatic Control.

[23]  Vijay Kumar,et al.  Controlling formations of multiple mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[24]  Marcia McNutt,et al.  The hunt for MH370 , 2014, Science.

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

[26]  Wei Ren,et al.  Multi-vehicle consensus with a time-varying reference state , 2007, Syst. Control. Lett..

[27]  B. Schulz,et al.  Field results of multi-UUV missions using ranger micro-UUVs , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

[28]  Ilya V. Kolmanovsky,et al.  Robust Control of Constrained Linear Systems With Bounded Disturbances , 2012, IEEE Transactions on Automatic Control.

[29]  C. L. Philip Chen,et al.  Optimized Multi-Agent Formation Control Based on an Identifier–Actor–Critic Reinforcement Learning Algorithm , 2018, IEEE Transactions on Fuzzy Systems.

[30]  David M. Fratantoni,et al.  Multi-AUV Control and Adaptive Sampling in Monterey Bay , 2006, IEEE Journal of Oceanic Engineering.

[31]  C. L. Philip Chen,et al.  Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Cheng Liu,et al.  Integrated Line of Sight and Model Predictive Control for Path Following and Roll Motion Control Using Rudder , 2015 .

[33]  Weidong Zhang,et al.  Adaptive cooperative formation control of autonomous surface vessels with uncertain dynamics and external disturbances , 2018, Ocean Engineering.

[34]  Zhang Yu,et al.  Path-guided time-varying formation control with collision avoidance and connectivity preservation of under-actuated autonomous surface vehicles subject to unknown input gains , 2019, Ocean Engineering.

[35]  Lihua Xie,et al.  A New Distributed Model Predictive Control for Unconstrained Double-Integrator Multiagent Systems , 2018, IEEE Transactions on Automatic Control.

[36]  Nina Mahmoudian,et al.  Underwater multi-robot persistent area coverage mission planning , 2016, OCEANS 2016 MTS/IEEE Monterey.

[37]  Shuzhi Sam Ge,et al.  Unified iterative learning control for flexible structures with input constraints , 2018, Autom..

[38]  Dengxiu Yu,et al.  Automatic Leader–Follower Persistent Formation Generation With Minimum Agent-Movement in Various Switching Topologies , 2020, IEEE Transactions on Cybernetics.

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