Distributed adaptive practical time-varying tracking control for second-order nonlinear multi-agent system using neural networks

Practical adaptive time-varying formation tracking problems for second-order nonlinear multi-agent systems are investigated using neural networks, where the time-varying formation tracking error can be arbitrarily small. Different from the previous work, the states of followers form a predefined time-varying formation while tracking the states of the leader with unknown control input. Besides, the dynamics of each agent has heterogeneous nonlinearity. Firstly, for the case where the control input of the leader is unknown, a nonlinear practical time-varying formation tracking protocol using adaptive neural networks is proposed which is constructed using only local neighboring information. Secondly, sufficient conditions for the second-order nonlinear multi-agent systems to achieve practical time-varying formation are presented, where a novel practical time-varying formation tracking feasibility condition is given. Thirdly, an approach is presented to design the control parameters for distributed practical formation tracking control protocol. The stability of the closed-loop system is proven by using the Lyapunov stability theory. Finally, simulation results are given to illustrate the effectiveness of the obtained results.

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

[2]  Nathan van de Wouw,et al.  A virtual structure approach to formation control of unicycle mobile robots using mutual coupling , 2011, Int. J. Control.

[3]  Zhang Ren,et al.  Time-varying formation tracking for second-order multi-agent systems with one leader , 2015, 2015 Chinese Automation Congress (CAC).

[4]  Lei Wang,et al.  Bounded synchronization of a heterogeneous complex switched network , 2015, Autom..

[5]  Shuzhi Sam Ge,et al.  Adaptive neural control of uncertain MIMO nonlinear systems , 2004, IEEE Transactions on Neural Networks.

[6]  Wei Ren,et al.  Collective rotating motions of second-order multi-agent systems in three-dimensional space , 2011, Syst. Control. Lett..

[7]  Liu Yang,et al.  Leader-following consensus protocol for second-order multi-agent systems using neural networks , 2008, 2008 27th Chinese Control Conference.

[8]  Zhang Ren,et al.  Distributed Time-Varying Formation Tracking Analysis and Design for Second-Order Multi-Agent Systems , 2017, J. Intell. Robotic Syst..

[9]  Kao-Shing Hwang,et al.  A Simple Scheme for Formation Control Based on Weighted Behavior Learning , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Xin Yao,et al.  Cooperative Coevolutionary Algorithm-Based Model Predictive Control Guaranteeing Stability of Multirobot Formation , 2015, IEEE Transactions on Control Systems Technology.

[11]  Zhang Ren,et al.  Time-Varying Formation Tracking for Second-Order Multi-Agent Systems Subjected to Switching Topologies With Application to Quadrotor Formation Flying , 2017, IEEE Transactions on Industrial Electronics.

[12]  Dirk Söffker,et al.  Data-driven stabilization of unknown nonlinear dynamical systems using a cognition-based framework , 2016, Nonlinear Dynamics.

[13]  Mohammad Bagher Menhaj,et al.  Communication free leader-follower formation control of unmanned aircraft systems , 2016, Robotics Auton. Syst..

[14]  Rajesh Kumar,et al.  A Cooperative Network Framework for Multi-UAV Guided Ground Ad Hoc Networks , 2015, J. Intell. Robotic Syst..

[15]  Mark W. Spong,et al.  Collision-Free Formation Control with Decentralized Connectivity Preservation for Nonholonomic-Wheeled Mobile Robots , 2015, IEEE Transactions on Control of Network Systems.

[16]  David J. N. Limebeer,et al.  Linear Robust Control , 1994 .

[17]  Jinde Cao,et al.  Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control , 2017, Neural Networks.

[18]  Lihua Xie,et al.  Decentralized Multi-UAV Flight Autonomy for Moving Convoys Search and Track , 2017, IEEE Transactions on Control Systems Technology.

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

[20]  Zhaoxia Peng,et al.  Distributed consensus-based formation control for nonholonomic wheeled mobile robots using adaptive neural network , 2016 .

[21]  Quanmin Zhu,et al.  A framework of neural networks based consensus control for multiple robotic manipulators , 2014, Neurocomputing.