A multi-step predictive control method based on relevance vector machine(RVM) regression error compensation was put forward to control aero-engine distributed control system(DCS) with stochastic and bounded time-delay.The neural network nonlinear auto regressive moving average(NARMA) model of the aero-engine DCS was established to predict the future output.An improved multi-step prognostic algorithm based on RVM regression was presented to estimate prognostic error series which were compensated for the NARMA model.Finally

a neural network inverse controller was designed

and rolling optimization of the parameters was conducted by prognostic output and the expectations.Simulation results illustrate the control strategy avoids the effect of stochastic and bounded time-delay on the control system.The controller has favorable dynamic tracking and robust performance.The absolute steady error of low-pressure rotor speed step response is less than 0.04%

and the response time is less than 0.3 s.