A MOGWO-based multi-objective optimal tuning strategy for model predictive direct thrust control architecture in gas turbine engine

In modern gas turbine engine control, Direct Thrust Control (DTC) is an effective method for achieving the desired thrust. Model Predictive Control (MPC) has the characteristics of handling constraints while accomplishing command tracking, making it a promising approach for implementing DTC. However, since the performance of DTC is highly sensitive to MPC’s tuning parameters, developing an efficient optimization strategy for these parameters becomes imperative. Therefore, a Model Predictive Direct Thrust Control (MP-DTC) architecture is designed, and its asymptotic stability is proven. Additionally, the influence of the tuning parameters on control performance is analyzed. Then, a tuning strategy for MP-DTC architecture is proposed. This strategy combines the objectives of DTC to design a Multi-Objective Optimization (MOO) index function, and uses Multi-Objective Grey Wolf Optimizer (MOGWO) to solve its Pareto front and obtain the tuning parameters. In the Hardware-in-Loop (HIL) experiments, the proposed MPDTC architecture achieves the shortest settling time and smallest overshoot compared to the latest DTC scheme. Its MOGWO-based tuning strategy provides more Pareto-optimal solutions, ensuring optimal selection based on rapidity and stability, and maintains precise DTC even under component degradation and various operating conditions, thereby providing robustness, optimality, and generalizability.