Self-scheduled direct thrust control for gas turbine engine based on EME approach with bounded parameter variation

Direct Thrust Control (DTC) is effective in dealing with the mismatch between thrust and rotor speed in traditional engine control. Among the DTC architecture, model-based thrust estimation method has less arithmetic consumption and better real-time performance. In this paper, a direct thrust controller design approach for gas turbine engine based on parameter dependent model is proposed. In order to ensure the stability of DTC control system based on parameter dependent model, there are usually conservatism detects. For the purpose of reducing the conservatism in the solution process of filter and controller, an Equilibrium Manifold Expansion (EME) model with bounded parameter variation of engine is established. The design conditions of Kalman filter for discrete-time EME system are introduced, and the proposed conditions have a certain suppression effect on the input noise of the system with bounded parameter variation. The engine thrust estimator stability and H∞ filtering problems are solved by the polytopic quadratic Lyapunov function based on the Linear Matrix Inequalities (LMIs). To meet the performance requirements of thrust control, the Grey Wolf Optimization (GWO) algorithm is applied to optimize the PID control parameters. The proposed method is verified on a Hardware-in-Loop (HIL) platform. The simulation results demonstrate that the DTC framework can ensure the stability of engine closed-loop system in large range deviation tests. The filter and controller solution method considering the parameter variation boundary can obtain a solution that makes the system have better performance parameters. Moreover, the proposed filter has better thrust estimation performance than the traditional Kalman filter under the condition of sensor noise. Compared with Augmented Linear Quadratic Regulator (ALQR) controller, the PID controller optimized by GWO has a faster response in simulation.