Event-Triggered Observer-Based H∞ Optimal Tracking Formation Control for ITSs With Longitudinal-Lateral Slips and Disturbances

Distributed control has been extensively extended to networked intelligent transport systems (ITSs), addressing challenges such as unknown internal dynamics, output feedback, external disturbances, and dead-zone inputs. However, existing studies have not considered longitudinal-lateral slip dynamics, which affect yaw angle measurements due to general disturbances rather than Gaussian noise. This paper, therefore, aims to design an $\boldsymbol {\mathcal {H}}_{\boldsymbol {\infty } }$ optimal tracking formation control scheme resisting the unfavorable factors. Firstly, the event-triggered (ET) observers are proposed to estimate unavailable states and measurement disturbances, simultaneously such that the parameters are adjusted only when a triggering condition is violated. Secondly, a robust optimal control algorithm is built based on the development of a three-player zero-sum game theory and the integral reinforcement learning principle, where the control input player cooperates with the dead-zone compensation player to minimize a $\boldsymbol {\mathcal {L}_{2}}$ -gain consensus cost function while the external disturbance player tries to maximize it. The algorithm guarantees that the compensation laws of the dead-zone disturbance are approximated, and the largest unknown upper bounds to reject external disturbances are determined. In addition, the Zeno phenomenon and the persistence of excitation are excluded, and errors are uniformly ultimately bounded. Finally, the utilities of the observers and controllers are comparatively validated by the numerical simulation results of a networked ITS group.