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

For constructing a distributed consensus formation scheme for the two-wheel mobile robots with directed 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 control 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 optimizer. 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.