Integral Terminal Sliding-Mode Formation Control for Uncertain Heterogeneous Networked Mecanum-Wheeled Omnidirectional Robots

In this paper, an integral terminal sliding-mode (ITSM) consensus control method is presented for a team of uncertain, networked, heterogeneous Mecanum-wheeled omnidirectional robots (MWORs) moving together in formation. After the description of the dynamic model of each kind of MWOR by a set of unified multivariable vector state equations, the interconnection structure of the multi-robots is modeled by a directed and strongly connected graph. An ITSM formation control is proposed to achieve finite-time formation control and trajectory tracking in presence of robot uncertainties. The usefulness and superiority of the proposed method are well exemplified by conducting three computer simulations on cooperative formation of four heterogeneous MWORs with a virtual leader.

[1]  W. Marsden I and J , 2012 .

[2]  Yeong-Hwa Chang,et al.  Fuzzy Sliding-Mode Formation Control for Multirobot Systems: Design and Implementation , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Xinghuo Yu,et al.  Terminal sliding modes with fast transient performance , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  Xinghuo Yu,et al.  Terminal sliding mode control of MIMO linear systems , 1997 .

[6]  Richard M. Murray,et al.  Recent Research in Cooperative Control of Multivehicle Systems , 2007 .

[7]  Ching-Chih Tsai,et al.  Distributed Consensus Formation Control with Collision and Obstacle Avoidance for Uncertain Networked Omnidirectional Multi-robot Systems Using Fuzzy Wavelet Neural Networks , 2017, Int. J. Fuzzy Syst..

[8]  Jiann-Fuh Chen,et al.  Digital-signal-processor-based DC/AC inverter with integral-compensation terminal sliding-mode control , 2011 .

[9]  Abhijit Das,et al.  Cooperative Control of Multi-Agent Systems , 2014 .

[10]  Lynne E. Parker,et al.  Distributed Intelligence: Overview of the Field and Its Application in Multi-Robot Systems , 2008, AAAI Fall Symposium: Regarding the Intelligence in Distributed Intelligent Systems.

[11]  Wei Ren,et al.  Information consensus in multivehicle cooperative control , 2007, IEEE Control Systems.

[12]  Xinghuo Yu,et al.  Model reference adaptive control systems with terminal sliding modes , 1996 .

[13]  C. L. Philip Chen,et al.  Fuzzy Observed-Based Adaptive Consensus Tracking Control for Second-Order Multiagent Systems With Heterogeneous Nonlinear Dynamics , 2016, IEEE Transactions on Fuzzy Systems.