An improved multivariable generalized predictive control algorithm for direct performance control of gas turbine engine

摘要

Providing thrust for the aircrafts is the primary task of the gas turbine engine. Accurate and safe thrust controller design has always been the focus of the research. In this paper, an improved control algorithm for direct performance control of the gas turbine engine is proposed to realize the on-line estimation and tracking of the performance parameters. The controlled plant, identification module, controller and diagnosis module are integrated as a complete system, which has two loops. First, the adaptive model calculates the engine's performance parameters (thrust and compressor surge margin) as the feedback of the controller; then a Quantum-behaved Particle Swarm Optimization (QPSO) based multivariable generalized predictive controller (MGPC) is adopted as the main controller. To solve complex nonlinear optimization problems with constraints, the basic QPSO algorithm is improved from two aspects, global average best position and initial global best position. In addition, a penalty factor is added to the cost function to realize the limit protection of the rotor speed and exhaust gas temperature (EGT). Compared with the traditional control methods, the main contribution of this paper to realize the direct performance control without the conversion by the rotor speed, and the control system is responsible for limit protection and fault diagnosis in the meanwhile. Finally, simulations are performed to investigate the response of the performance parameters, the effects of the controller parameters, the ability of fault tolerance of the controller, and robustness against measurement noise and model uncertainties. The results show that the proposed scheme is robust and can achieve accurate regulation of the performance parameters and the limited outputs within the safe range, whether there is any fault or not. (C) 2019 Elsevier Masson SAS. All rights reserved.