In this paper, for controlling the time-varying consensus formation (TVCF) of multiply nonholonomic robots under switching information transforming topology, a generalized consensus formation system, which is controlled by a neural-dynamic-optimized distributed model predictive control (NDMPC) based consensus protocol strategy, is developed. The system consists of an auxiliary consensus maneuvering subsystem and a formation tracking subsystem. Through simultaneously stabilizing the generalized errors consisted of these two subsystems under the switching topologies, the consensus object is achieved. Within each sampling time, the NDMPC method can formulate and solve a constrained quadratic programming (QP) problem with time-varying desired formation pattern and get the optimal inputs for each robot in a distributed manner. The constraints of the system, as well as the switching structure brought by the changing topologies, can be tackled by utilizing the proposed method. In the end, numerical examples verify the effectiveness of the proposed formation control method.