Dynamic event-triggered adaptive neural control for MIMO nonlinear systems via a single parameter learning method

The dynamic event-triggered control issue for multi-input and multi-output (MIMO) nonlinear systems with unknown dynamics and disturbances is solved in this paper. It is the first attempt to design a dynamic event-triggered mechanism for MIMO systems, which can further reduce data transmission from controller to actuator, thereby producing fewer event. The unknown dynamics of MIMO systems are approximated by neural networks. Then, the composited uncertain vector consisting of approximated terms and disturbances is transformed into a linear parameterized form with a single parameter. In addition, the dynamic event-triggered adaptive control method is designed under the backstepping framework. Finally, the validity of the presented dynamic event-triggered control solution is verified by simulation example and comparison analysis.