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This paper presents an integrated framework for aircraft engines, which consists of three phases: modeling, control, and experimental testing. The engine is formulated as an uncertain T–S fuzzy model. By a hierarchical dynamical parameter clustering, the number and premise variables of fuzzy rules are optimized, which keeps the engine's prime and representative dynamics. For each fuzzy rule, a global stability-guaranteed method is developed for the identification of the consequent uncertain dynamic model. The resulting stable T–S fuzzy model accurately approximates the actual engine dynamics in the operation space. Based on this fuzzy model, a new robust control is constructed with hierarchical compensators. The control parameters take advantage of the fuzzy blend of engine prime dynamics and uncertainty thresholds. Extensive hardware-in-loop (HIL) experimental tests in the flight envelope and a flight task cycle demonstrate the effectiveness and real-time performance of the proposed control. The settling times and overshoots of engine response are suppressed to be under 2.5 s and 10%, respectively. [ABSTRACT FROM AUTHOR]

The paper proposes a novel trajectory tracking control for uncertain mechanical systems. We reformulate the trajectory tracking control as a constraint-following problem. A new set of servo constraints are constructed to fulfill the requirement of trajectory tracking. By the advantage of the second-order form of constraints, the constraint forces required in the constraint-following are derived in an explicit form. The uncertainty considered in the mechanical system is time-varying and bounded. But the bound is unknown. An adaptation mechanism with leakage and dead-zone is designed. Based on the adaptation, a new class of robust control is proposed. The performance of the resulting controlled system, including uniform boundedness and uniform ultimate boundedness, is guaranteed. [ABSTRACT FROM AUTHOR]

This paper presents a data-based Takagi-Sugeno (T–S) fuzzy modeling approach for aircraft engines in the flight envelope. We propose a series of T–S fuzzy models for engines with flight conditions as premises and engine linear dynamic models as consequences. By engine dynamic clustering, we determine rough T–S fuzzy models to approximate the nonlinear dynamics of engines in the flight envelope. After that, the maximum–minimum distance-based fuzzy c-means (MMD-FCM) algorithm comes to refine the fuzzy rules and the least square method (LSM) comes to identify premise parameters for each single rough model. The proposed MMD-FCM algorithm guarantees the refined results are stable and reasonable, and the identification improves the accuracy of the steady and transient phases. The model verification showed that the T–S fuzzy models for engines had a high accuracy with a steady error less than 5%, and that the root mean squared error (RMSE) of transient errors was less than 8 × 10−4 with good generalization ability in the flight envelope. [ABSTRACT FROM AUTHOR]