Data-driven consistent control with data compensation for a class of unknown nonlinear multiagent systems with constraints

To solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model-free adaptive control algorithm with data compensation under constraint conditions (COM-cMFAC) is proposed in this manuscript. In the COM-cMFAC algorithm, the pseudo partial derivative (PPD), which is a time-varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM-cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM-cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment.