Integrated nonholonomic multi-robot consensus tracking formation using neural-network-optimized distributed model predictive control strategy

作者：Xiao, Hanzhen; Chen, C. L. Philip^*; Lai, Guanyu; Yu, Dengxiu; Zhang, Yun

来源：NEUROCOMPUTING, 2023, 518: 282-293.

DOI：10.1016/j.neucom.2022.11.007

• Wheeled nonholonomic mobile robot
• Consensus formation
• Distributed model predictive control (DMPC)
• Primal-dual neural networks (PDNN)

For constructing a distributed consensus formation scheme for the two-wheel mobile robots with direc-ted communication topology and nonholonomic constraints, in this work, an integrated leader-follower consensus formation framework using neural-network-optimized distributed model predictive control (NNODMPC) strategy is presented. The proposed leader-follower formation framework can be regarded as integrating a consensus trajectories planning subsystem (CTPS) and a consensus tracking subsystem. The CTPS can plan the consensus trajectories for the mobile robots through a leader-follower structure, and in the consensus tracking subsystem, the robots are guided to the consensus. To simultaneously con-trol these two subsystems, the NNODMPC based protocol is applied. The errors and constraints of the integrated system can be incorporated and formulated into a constrained quadratic programming (QP) problem whose optimal solution can be obtained by a primal-dual neural networks (PDNN) QP opti-mizer. Stability analysis illustrates the convergence of the proposed DMPC-based consensus formation system. The novelties of this work are rooted in the construction of a generalized consensus formation scheme for the practical wheeled robots with nonholonomic constraints, the formulation of an MPC-based distributed consensus controller and the consideration of system constraints. The experimental results on the mobile robots are conducted to verify the effectiveness of the proposed strategy.

• 单位
  广东工业大学; 西北工业大学