Synchronized control with neuro-agents for leader-follower based multiple robotic manipulators

In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on the leader-follower network communication topology. The justification of introducing two adaptive Radial Basis Function Neural Networks (RBF NN), also called neuro-agents, is to facilitate the whole control system design and analysis. Otherwise such design is impossible with classical analytical procedure. The first agent is the neuro-compensator to accommodate uncertainty associated with the follower manipulators, and the second agent is the neuro-estimator to obtain acceleration of the leader manipulator. Correspondingly the stability analysis of the designed control system is formulated with Lyapunov method. Finally numerical bench tests under various critical conditions are conducted to validate the effectiveness of the proposed approach.

[1]  Zhihong Man,et al.  Robust Finite-Time Consensus Tracking Algorithm for Multirobot Systems , 2009, IEEE/ASME Transactions on Mechatronics.

[2]  L. P. J. Veelenturf,et al.  Analysis and applications of artificial neural networks , 1995 .

[3]  Long Wang,et al.  Geometric topology based cooperation for multiple robots in adversarial environments , 2008 .

[4]  Rogelio Lozano,et al.  Synchronization of bilateral teleoperators with time delay , 2008, Autom..

[5]  Dong Sun.  Position synchronization of multiple motion axes with adaptive coupling control , 2003, Autom..

[6]  H Henk Nijmeijer,et al.  Synchronization of Mechanical Systems , 2003 .

[7]  Rui P. Rocha,et al.  Cooperative Multi-Robot Systems A study of Vision-based 3-D Mapping using Information Theory , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Dong Sun,et al.  Adaptive synchronized control for coordination of multirobot assembly tasks , 2002, IEEE Trans. Robotics Autom..

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

[10]  Marco A. Arteaga,et al.  Dynamic model and simulation of cooperative robots: a case study , 2005, Robotica.

[11]  Ahmad B. Rad,et al.  Indirect adaptive tracking control of a nonholonomic mobile robot via neural networks , 2012, Neurocomputing.

[12]  Wen-Hong Zhu.  On adaptive synchronization control of coordinated multirobots with flexible/rigid constraints , 2005, IEEE Transactions on Robotics.

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

[14]  Rong-Jong Wai,et al.  Tracking control based on neural network strategy for robot manipulator , 2003, Neurocomputing.

[15]  Randal W. Beard,et al.  Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications , 2007, Communications and Control Engineering.

[16]  Yiguang Hong,et al.  Distributed Observers Design for Leader-Following Control of Multi-Agent Networks (Extended Version) , 2017, 1801.00258.

[17]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[18]  Feng Gao,et al.  Robust adaptive terminal sliding mode-based synchronised position control for multiple motion axes systems , 2009 .

[19]  Myung Jin Chung,et al.  Adaptive controller of a master-slave system for transparent teleoperation , 1998, J. Field Robotics.

[20]  Dong Sun,et al.  Synchronization and Control of Multiagent Systems , 2016 .

[21]  Haruhisa Kawasaki,et al.  Decentralized adaptive coordinated control of multiple robot arms without using a force sensor , 2006, Autom..

[22]  P. Olver.  Nonlinear Systems , 2013 .

[23]  Tao Li,et al.  Adaptive RBF neural-networks control for a class of time-delay nonlinear systems , 2008, Neurocomputing.

[24]  Wail Gueaieb,et al.  Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics , 2007, Autom..

[25]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[26]  F. Karray,et al.  A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators , 2007, IEEE/ASME Transactions on Mechatronics.

[27]  Marco A. Arteaga,et al.  Decentralized control of cooperative robots without velocity-force measurements , 2005 .

[28]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[29]  Henk Nijmeijer,et al.  Mutual synchronization of robots via estimated state feedback: a cooperative approach , 2004, IEEE Transactions on Control Systems Technology.

[30]  Quanmin Zhu,et al.  Low-pass-filter-based position synchronization sliding mode control for multiple robotic manipulator systems , 2011 .

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

[32]  Yunhui Liu,et al.  Cooperation control of multiple manipulators with passive joints , 1999, IEEE Trans. Robotics Autom..

[33]  Boubaker Daachi,et al.  Adaptive neural controller for redundant robot manipulators and collision avoidance with mobile obstacles , 2012, Neurocomputing.

[34]  Guido Bugmann,et al.  Normalized Gaussian Radial Basis Function networks , 1998, Neurocomputing.