Formation Control With Obstacle Avoidance for a Class of Stochastic Multiagent Systems

This paper addresses formation control with obstacle avoidance problem for a class of second-order stochastic nonlinear multiagent systems under directed topology. Different with deterministic multiagent systems, stochastic cases are more practical and challenging because the exogenous disturbances depicted by the Wiener process are considered. In order to achieve control objective, both the leader-follower formation approach and the artificial potential field (APF) method are combined together, where the artificial potential is utilized to solve obstacle avoidance problem. For obtaining good system robustness to the undesired side effects of the artificial potential, $H_\infty$ analysis is implemented. Based on the Lyapunov stability theory, it is proven that control objective can be achieved, of which obstacle avoidance is proven by finding an energy function satisfying that its time derivative is positive. Finally, a numerical simulation is carried out to further demonstrate the effectiveness of the proposed formation schemes.

[1]  Shaocheng Tong,et al.  Observer-Based Adaptive Fuzzy Backstepping Control for a Class of Stochastic Nonlinear Strict-Feedback Systems , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Tianping Chen,et al.  New approach to synchronization analysis of linearly coupled ordinary differential systems , 2006 .

[3]  A. Isidori,et al.  Disturbance attenuation and H/sub infinity /-control via measurement feedback in nonlinear systems , 1992 .

[4]  Long Wang,et al.  Finite-time consensus for stochastic multi-agent systems , 2011, Int. J. Control.

[5]  Chai Wah Wu,et al.  Synchronization in Complex Networks of Nonlinear Dynamical Systems , 2008 .

[6]  W. McEneaney.  Robust/ H ∞ filtering for nonlinear systems , 1998 .

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

[8]  Shihua Chen,et al.  Pinning synchronization of the complex networks with non-delayed and delayed coupling , 2009 .

[9]  Bai Yongqiang,et al.  Obstacle avoidance for multi-agent systems based on stream function and hierarchical associations , 2012, Proceedings of the 31st Chinese Control Conference.

[10]  Shengyuan Xu,et al.  Brief paper H∞ output feedback control for uncertain stochastic systems with time-varying delays , 2004 .

[11]  Wei Kang,et al.  Co-ordinated attitude control of multi-satellite systems† , 2002 .

[12]  Weihai Zhang,et al.  State Feedback HINFINITY Control for a Class of Nonlinear Stochastic Systems , 2006, SIAM J. Control. Optim..

[13]  George J. Pappas,et al.  Stable flocking of mobile agents, part I: fixed topology , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[14]  A. Astolfi.  Disturbance Attenuation and H,-Control Via Measurement Feedback in , 1992 .

[15]  Zhang Ying,et al.  Leader-follower formation control and obstacle avoidance of multi-robot based on artificial potential field , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[16]  Alfredo Ferro,et al.  Obstacles constrained group mobility models in event-driven wireless networks with movable base stations , 2011, Ad Hoc Networks.

[17]  Kazushi Nakano,et al.  Group motion control of multi-agent systems with obstacle avoidance: column formation under input constraints , 2015, Artificial Life and Robotics.

[18]  Petros A. Ioannou,et al.  Vehicle following control design for automated highway systems , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[19]  Licheng Jiao,et al.  Finite-time stability theorem of stochastic nonlinear systems , 2010, Autom..

[20]  K. Mathiyalagan,et al.  Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks , 2012 .

[21]  Abdelkader Abdessameud,et al.  Formation control of VTOL Unmanned Aerial Vehicles with communication delays , 2011, Autom..

[22]  Kar-Han Tan,et al.  High Precision Formation Control of Mobile Robots Using Virtual Structures , 1997, Auton. Robots.

[23]  Tao Li,et al.  Consensus Conditions of Multi-Agent Systems With Time-Varying Topologies and Stochastic Communication Noises , 2010, IEEE Transactions on Automatic Control.

[24]  C. Yung,et al.  H filtering and solution bound for non-linear systems , 2001 .

[25]  Zhong-Ping Jiang,et al.  Decentralized adaptive output-feedback stabilization for large-scale stochastic nonlinear systems , 2007, Autom..

[26]  Qiang Wang,et al.  Flocking with obstacle avoidance and connectivity maintenance in multi-agent systems , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[27]  Xin-Ping Guan,et al.  Formation Control and Obstacle Avoidance for Multi-Agent Systems Based on Virtual Leader-Follower Strategy , 2017, Int. J. Inf. Technol. Decis. Mak..

[28]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[29]  Liu Yutian,et al.  Obstacle Avoidance with Formation Kept for Multi-agent , 2015 .

[30]  Yang Li,et al.  Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[31]  Tamio Arai,et al.  A distributed control scheme for multiple robotic vehicles to make group formations , 2001, Robotics Auton. Syst..

[32]  Hai-Bo Ji,et al.  Adaptive output-feedback tracking of stochastic nonlinear systems , 2006, IEEE Transactions on Automatic Control.

[33]  Ji Yang Dai,et al.  Research on Multi-Agent Formation’s Obstacle Avoidance Problem Based on Three-Dimensional Vectorial Artificial Potential Field Method , 2014 .

[34]  Bor-Sen Chen,et al.  H∞ fuzzy estimation for a class of nonlinear discrete-time dynamic systems , 2001, IEEE Trans. Signal Process..

[35]  W. Zhang,et al.  State Feedback H_∞Control for a Class of Nonlinear Stochastic Systems , 2009 .

[36]  Wenwu Yu,et al.  Second-Order Consensus for Multiagent Systems With Directed Topologies and Nonlinear Dynamics , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  Chenguang Yang,et al.  Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities , 2017, IEEE Transactions on Industrial Electronics.