Xianghua Huang’s research while affiliated with Nanjing University of Aeronautics and Astronautics and other places

Due to the complex nature of a variable cycle engine (VCE), which has numerous control variables and working modes across a broad flight envelope, coupled with the whole engine’s degradation, the analytical redundancy method based on component-level models may not provide an accurate estimation of the sensors. Variable-weights-biases neural network (VWB Net) is proposed to construct VCE’s analytical redundancy. Unlike conventional networks whose weights and biases are fixed, VWB Net’s variable-weights and variable-biases are functions of input which greatly increase its nonlinear mapping capability by integrating input information. Variable-biases can also be used to eliminate the error between actual sensor output and estimated value quickly at the terminal node. Compared with the BP network and Dense net, VWB Net has fewer parameters, faster calculation speed, and higher accuracy. Digital simulation results of VCE parameter estimation demonstrate that VWB Net’s average relative errors are under 0.27% with calculation and parameter efficiency at least 166 times higher than that of Dense net. Hardware in the loop simulation further verifies VWB Net’s estimation accuracy and real-time calculation.

The active jet noise control received significant attention due to the little influence it has on the engine performance. The active jet noise control is a multivariable problem because it needs to achieve the simultaneous closed-loop control of jet noise and engine performance. Model predictive control (MPC) has great application potentials in the field of multivariable control of aero-engines, but the real-time performance of MPC is intractable. This paper proposed an active jet noise controller of a turbofan engine, based on explicit model predictive control (EMPC). An integrated model of turbofan engine and jet noise, which calculates the engine parameters and jet noise in real time, was established. The online computational burden of MPC was transferred to offline computation using multi-parametric quadratic programming (MPQP). To improve the efficiency of the online positioning algorithm, the sequence search method was replaced by a binary search tree. Step simulations were performed to test the effectiveness of the proposed controller. The results show that the proposed EMPC controller not only achieves the simultaneous control of jet noise and the turbofan engine, but also improve the real-time performance greatly.