Composite Learning-Based Incremental Neural Control for 2-DOF Helicopter with Adaptive Dynamic Event-Triggering and Input Saturation

This study proposes an incremental neural network adaptive control algorithm based on composite learning for a two-degree-of-freedom (2-DOF) helicopter system characterised by dynamic event triggering and input saturation. Firstly, by integrating a composite learning strategy within the incremental neural network control framework, the study aims to overcome the challenges posed by system dynamic uncertainties. The proposed novel update algorithm effectively incorporates estimation error terms into the weight adaptation process, thereby improving the approximation capability for system dynamics while alleviating the dependence on the classical persistent excitation condition. In addition, to reduce the communication load between the controller and the actuator, we introduce an adaptive dynamic event-triggered mechanism. Furthermore, a saturation-resistant auxiliary system is constructed to address the input saturation phenomenon present in the system. Subsequently, the system is proven to be semi-globally consistent and bounded stable via Lyapunov functions. Finally, the effectiveness of the control strategy proposed in this study is verified through simulation.