A novel direct performance adaptive control of aero-engine using subspace-based improved model predictive control

Abstract

The direct performance predictive control method can fully improve the engine's operating performance. Compared with the traditional control methods, it has a better control effect. However, it urgently needs to solve the problems of how to achieve the predictive control applicable for any operating point of the engine and how to further reduce the calculation cost of performance parameter estimation. Therefore, this paper first proposes a subspace-based improved model predictive control method. The subspace identification method is used to identify the past I/O data set of the engine offline to obtain the approximate predictive model, and then the predictive model is updated in real-time according to the currently collected I/O data online to adapt to the current actual operating point of the engine. Thus, the engine's direct performance adaptive control is successfully realized based on the predictive control using this obtained predictive model in full life cycle, full envelope, and full state in this paper. This method reduces the online computational burden through the Givens rotation transformation algorithm, and the design process is simple. Compared with the existing methods, it not only improves the robustness, adaptability, and independence of the engine model but also perfects the control performance. Secondly, aiming at the problems, such as complex design and a tremendous amount of calculation of the current direct performance estimator, a high-precision real-time estimation method of nonlinear onboard performance parameters based on spherical unscented Kalman filter is also proposed. Compared with the existing estimation methods, its computational complexity is reduced by one time while maintaining accuracy. Finally, a fully digital simulation system of aero-engine's direct performance adaptive predictive control is established. The simulation results show that the dynamic response time from idle to maximum state is 3.48 s, the steady-state error is ±0.2%, and there is no overshoot. Compared with the traditional control, the dynamic performance is improved by 32%.