Self-Triggered Synchronized Path Maneuvering of Constrained Uncertain MIMO Nonlinear Systems Based on Command Optimization

This article addresses the synchronized path maneuvering problem of uncertain nonlinear systems in multiple-input multiple-output (MIMO) strict-feedback form subject to state and input constraints, unknown input gains, and limited communication resources. An aperiodic synchronized path maneuvering control architecture is proposed based on data-driven neural estimation, command optimization, and self-triggered path coordination. Specifically, a data-driven neural predictor is designed for identifying unknown input gains, in addition to unknown nonlinear functions. In the path maneuvering control law design, an optimization-based command governor is used to obtain the optimal virtual and actual control laws within the state and input constraints. In the path variable coordination part, a self-triggered path update law is developed to reduce the transmission load. Based on cascade stability theory, the closed-loop system is proven to be input-to-state stable, and all signals are uniformly ultimately bounded. An application to formation control of multiple vehicles is provided to substantiate the efficacy of the proposed command optimization method.