Multi-step predictive compensated intelligent control for aero-engine wireless networked system with random scheduling

Abstract

Within the future application of wireless network for the aero-engine control problem, resource constraints (caused by the limitation of hardware), network traffic restriction, interference and long time-delay, must be considered as one of the difficulties to be solved, thus, the network connection and transmission efficiency can be ensured. In order to ensure the performance of aero-engine nonlinear wireless networked control system in consideration with complicated flight conditions and adverse network environments, a SVM (Support Vector Machine) intelligent inverse controller with a reference model was established. To reduce the offline modeling error with time-varying random scheduling strategy and disturbance, a LSTM (Long Short Term Memory) online multi-step error compensation was designed. Training result, on the four optimal conditions chosen in the flight envelop, provided the model and controller establishing is completed. After introduced into the overall system, checking result, showed the errors and the output with/without LSTM compensation on the other four random conditions, provided the control performance under the three kind of adverse wireless network environments. for comparison, other intelligent algorithms were also adopted to predict the multi-step errors in the reference model, then the accuracy and computing time provided the advantage of LSTM algorithm. The method and strategy proposed in this paper ensure the aero-engine safe working at the price of some control performance loss on the adverse wireless network environments.