The dynamic characteristics of the turbofan engine vary greatly in the full flight envelope, which makes the problem of dynamic uncertainty and input uncertainty very prominent. This brings different degrees of performance impact to the engine control system and even makes it lose stability. This paper proposes an adaptive variable parameter control method for dealing with multivariable dynamic uncertainty and input uncertainty. In this paper, the dynamic uncertainty and input uncertainty are mathematically converted into standard matched uncertainty, which can be handled more conveniently. Firstly, in the state space model, for the case where the number of state variables is less than or equal to the number of input variables and the input matrix satisfies the full-rank condition of the row, the existence of the right pseudo-inverse matrix of the input matrix can be guaranteed. So the dynamic uncertainty can be separated from the system matrix, and the input uncertainty can be separated from the input matrix. Thus these uncertainties are equivalently transformed into parametric matched uncertainty. Then the matched uncertainty model with two vectors of bounded basis functions is established. Secondly, the Lyapunov quadratic function is constructed by the closed-loop tracking error vector and the adaptively adjustable control parameter estimation errors, and the Lyapunov stability constraint is considered. Then, under the premise of considering the dynamic characteristics of the actuator, an adaptive control algorithm for multivariable matched uncertainty model of turbofan engine is derived. Finally; ground and high altitude simulations are carried out on the dual-loop control system based on the nonlinear dynamic model of the turbofan engine. The results show that the control system has robust stability and anti-interference performance for dynamic uncertainty and input uncertainty of turbofan engine in the full flight envelope. The fan speed control loop basically achieves no static error tracking. The dynamic error of the core speed control loop is less than 0.6% and the steady state error is less than 0.05%. By introducing stronger parameter change rate information to the controller; its performance can be further improved; and the transient state control is more stable.
展开▼
