Event-triggered adaptive NN tracking control with dynamic gain for a class of unknown nonlinear systems

Abstract

An event-triggered tracking control problem is investigated for a class of unknown nonlinear systems in this paper. First, to approximate the unknown function, the radial basis function neural network is used. Then, we propose an event-triggered adaptive neural network control scheme with dynamic gain. Both the event-triggered mechanism in the controller-to-actuator channel and the dynamic gain in the sensor-to-controller channel are considered to reduce the communication load between the controller and the actuator and in the meantime to alleviate the burden of parameter adjustment as well. This scheme makes full use of the advantage that the dynamic gain is driven by the tracking error to ensure that the system output signal can track the reference signal within a prespecified accuracy without adjusting the parameters in use. Moreover, a detailed theoretical proof is given to illustrate that Zeno behavior can be excluded under the designed event-triggered controller. Finally, a simulation example is provided to show the effectiveness of our control scheme.