The development of advanced control systems is motivated to achieve the complicated aircraft engine control, so a novel semi-alternative optimization strategy based model predictive control is proposed. In this proposed controller, a nonlinear state-space model is generated as the predictive model on the basis of a baseline engine model that estimates the current operating state of engine with an extended Kalman filter every sampling instant, and a quadratic constrained problem rather than a higher order nonlinear problem is constructed based on this model. Moreover, a novel control sequence to be optimized based on semi-alternative optimization strategy is proposed, which includes two parts. The first part represents that all the control variables of the engine are optimized for the current sampling instant while the second part represents that different control variables are optimized alternatively for different future sampling instants over the control horizon. The proposed model predictive control method is implemented to a twin-spool turbofan engine. Simulation results demonstrate not only that the proposed method can achieve a smaller control error than the standard model predictive control algorithm does, but also that the proposed controller is more time-saving than the standard model predictive control, which can save up to about 61% optimization time when control horizon increases to nine.

先进控制系统的发展是为了实现复杂的飞机发动机控制，因此提出了一种新的基于模型预测控制的半交替优化策略。在这个提出的控制器中，一个非线性状态空间模型被生成作为预测模型，该模型基于一个基线发动机模型，该模型在每个采样时刻用一个扩展卡尔曼滤波器估计发动机的当前运行状态，以及一个二次约束问题，而不是一个基于该模型构造高阶非线性问题。此外，提出了一种新的基于半交替优化策略的待优化控制序列，包括两部分。第一部分表示针对当前采样时刻优化发动机的所有控制变量，而第二部分表示针对控制范围内的不同未来采样时刻交替优化不同的控制变量。所提出的模型预测控制方法应用于双转子涡扇发动机。仿真结果表明，与标准模型预测控制算法相比，所提出的方法不仅可以实现更小的控制误差，而且所提出的控制器比标准模型预测控制更省时，最多可节省61%左右的时间。控制范围增加到 9 时的优化时间。所提出的模型预测控制方法应用于双转子涡扇发动机。仿真结果表明，与标准模型预测控制算法相比，所提出的方法不仅可以实现更小的控制误差，而且所提出的控制器比标准模型预测控制更省时，最多可节省61%左右的时间。控制范围增加到 9 时的优化时间。所提出的模型预测控制方法应用于双转子涡扇发动机。仿真结果表明，与标准模型预测控制算法相比，所提出的方法不仅可以实现更小的控制误差，而且所提出的控制器比标准模型预测控制更省时，最多可节省61%左右的时间。控制范围增加到 9 时的优化时间。