To improve the learning process of multi-agent system and the robustness of the system to external disturbances， an iterative learning consensus control algorithm with feedback control was proposed. Firstly， the learning process of agents was improved by sharing the control input information among agents， and the robustness of the system was improved by designing a feedback controller when there were non-iterative repetitive disturbances outside the system. Then， by using the contraction mapping method， the system consensus was analyzed， and the convergence condition of the algorithm was derived strictly. Finally， the correctness and effectiveness of the algorithm was verified through simulations. Compared with the P-type algorithm， the improved algorithm has higher convergence speed and smoother convergence curve in the presence of external disturbances.

Aiming at the dynamic average consensus tracking problem of multi-agent systems with time-varying reference inputs， a proportional-integral consensus tracking algorithm was proposed. In the scenario of communication data between multi-agents being quantized， the average consensus tracking problem based on quantization was studied. Firstly， on the basis of the integral algorithm， a proportional link was introduced to make agents to track the average value of the reference inputs better by communicating with neighborhood agents under the constraints of the control agreement. Then， under the fixed， strongly connected and balanced topology structure， sufficient conditions for the multi-agent system asymptotically tracking to the average value of time-varying reference inputs without and with quantized information transmission data were obtained by using matrix analysis and Routh criteria respectively. Finally， numerical simulations verify the accuracy of the results and confirm the effectiveness of the proposed algorithm.