Stabilisation of event-triggered-based neural network control system and its application to wind power generation systems

Abstract

This study addresses the event-triggered (ET)-based stabilisation problem of neural-network-based control system (NNBCS) and illustrates the direct application to wind power generation system. In this regard, the novel ET-based controller algorithm is designed for NNBCS instead of sampled data controller (sampling will be initiated at a fixed rate regardless whether it is required or not) which reduces the computation complexity by avoiding the unnecessary details over the transmission. The novel stability and stabilisation conditions are expressed in terms of linear matrix inequalities that are derived through constructing the time-dependent Lyapunov functional candidate. For deepening the knowledge in the outcomes of the proposed conditions, the study numerically evaluates the dynamic models such as variable-speed wind turbine drive system, permanent magnet synchronous motors model and traditional inverted pendulum model and validates the effectiveness of the proposed controller scheme. Finally, a comparison result shows the superiority of the derived conditions.