Model-Free Output Consensus Control for Partially Observable Heterogeneous Multivehicle Systems

Abstract

Internet of Vehicles (IoV) is a typical application of Internet-of-Things (IoT) technology in the field of intelligent transportation systems. In the actual IoV, such as the autonomous vehicle fleet, there exists the problem of heterogeneous multivehicle coordination based on IoT communication. How to ensure the synchronization of multiple vehicles is a hot issue. In particular, when the system can only obtain a partial state of the vehicle, and does not know the dynamic model, including the vehicle itself and the companion model. To overcome these deficiencies, this article deals with the model-free output consensus control problem for a class of partially observable heterogeneous multivehicle systems (MVSs). Using measurable input/output data without any system knowledge, this article develops a $Q$ -function-based adaptive dynamic programming (ADP). First, an adaptive distributed observer is designed to estimate the output of the leader. The augmented state representation is built using historical measurable input/output data instead of the unmeasurable inner system state. Then, a $Q$ -function-based ADP method using measurable input/output data was introduced. The method is used to solve this distributed tracking control problem without the requirement for the MVSs dynamics. The convergence analysis of the proposed method is also given. To facilitate the implementation of the proposed method, an actor–critic framework is adopted to approximate the optimal $Q$ -functions and the optimal control policies. It shows that the approximated control policies achieve the distributed optimal tracking control. Finally, the simulation results verify the effectiveness of the developed method for solving multivehicle formation control problems.