Fuzzy logic-based adaptive tracking weight-tuned direct performance predictive control method of aero-engine

Direct performance predictive control is the most cutting-edge research in aero-engine control. The existing subspace-based improved model predictive control (SIMPC) effectively handled the problem of difficulty solving the prediction model in predictive control. However, the strong nonlinearity of aeroengine makes the controller with design parameters set constant invariant value unable to guarantee consistent control effect in all states. Even with general scheduling for design parameters, the controller's performance still behaves poorly. Therefore, this paper innovatively proposes an adaptive tuning method for the design parameters of the SIMPC controller to adapt to all engine states. Firstly, by analyzing the influence of each design parameter of the controller on the control effect, the tracking weight with strong sensitivity is selected as the dynamic tuning parameter. Then, three variables of the ratio of PLA angle, relative tracking error, and scale factor are successfully introduced to map the tracking weight value without adding additional design parameters. Thus, a fuzzy logic tuner based on these variables is then designed for adaptively tuning the tracking weight to maintain the controller's performance. Finally, results show that, compared with the existing methods, while keeping the maximum dynamic response performance, the proposed method eliminates the phenomenon of large overshoot and controls it within 2% required in the whole state range. Compared with the general scheduling method, the designed adaptive tracking weight with positive excitation caused by relative tracking error effectively shortens the response time, especially with an average performance improvement of 17% under small step changes. Whether the engine is in standard or fault conditions, the designed adaptive tracking weight is effective and feasible for undertaking the controller's consistent control effects in all states.(c) 2023 Elsevier Masson SAS. All rights reserved.