Evolutionary Optimization for Gain Tuning of Jet Engine Min-Max Fuel Controller
Evolutionary Optimization for Gain Tuning of Jet Engine Min-Max Fuel Controller
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DOI：
10.2514/1.b34185
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发表时间：
2011-09
影响因子：
1.9
通讯作者：
S. Jafari;M. Montazeri-Gh
中科院分区：
文献类型：
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作者：
S. Jafari;M. Montazeri-Gh
AS涡轮航空发动机（GTE）在扩展现代飞机的飞行能力方面发挥了重要作用。对于 GTE，必须确保发动机部件的稳定和安全运行，以便按照其设计的可操作性和性能水平运行。推力响应对于飞机来说也非常重要，无论是在响应速度还是实现足够推力方面[1]。因此，适当的控制系统一直是 GTE 提供监管和管理的重要组成部分。 GTE控制系统的主要任务是提供所需的性能，同时保持安全稳定的运行[2]。安全考虑因素包括流动不稳定性、高热负荷和高循环应力。换句话说，GTE控制要求包括用于设置和保持稳态推力的主燃料控制（稳态控制模式）和燃料加速和减速计划，以提供限制保护（物理限制控制模式）和燃气涡轮发动机的快速时间响应（瞬态控制模式）[3]。 GTE 控制策略的调查表明，过去二十年中，各种燃料控制方法已用于 GTE 燃料控制 [4-16]。这些研究表明，发动机控制的复杂性来自于需要尽可能接近其极限运行发动机。换句话说，燃气涡轮航空发动机控制系统的设计导致了一系列单输入单输出控制器。 PID、LQ、LQG、H1等多种方法被用作GTE控制系统的单反馈控制回路之一。在这种情况下，设计方法基于各种控制回路之间的选择方法。该策略被称为“最小-最大选择策略”，因为控制环路的选择是基于瞬态控制环路之间的最小-最大方法。最小最大方法是一种用于设计 GTE 燃油控制系统的工业方法。然而，为了提供改进的发动机性能以及针对物理限制的发动机保护，最小-最大选择策略最常见的复杂性之一是控制器增益调整。考虑到这种控制策略的非线性和开关性质，基于梯度的优化方法的增益调节性能较弱。因此，该问题需要基于进化算法的非梯度优化技术。在本文中，提出了应用进化算法来优化最小-最大燃料控制器参数。为此，首先解释燃气涡轮航空发动机的控制要求和约束。然后描述最小-最大燃料控制策略并设计初始最小-最大燃料控制器。随后，使用遗传算法（GA）调整初始最小-最大燃料控制器的参数，以满足发动机的要求和约束。在优化过程中，定义适应度函数以最小化稳定时间和燃料消耗，其中根据每个函数的重要性设置权重因子。此外，还开发了计算机模拟程序来研究单转子喷气发动机方法的有效性。仿真结果通过稳态和瞬态模式下的实验数据进行验证，以支持仿真模型。最后，提供结果来研究优化控制器的性能并评估该方法的有效性。
AS turbine aero engines (GTEs) have played a significant role in the expansion of the flight capabilities of modern aircraft. For a GTE, stable and safe operation of the engine components must be ensured in order to operate at the operability and performance level for which it is designed. The thrust response is also of great importance for an aircraft, both in terms of the speed of response and accomplishment of the adequate thrust [1]. Hence, an appropriate control system has always been an essential part of the GTEs to provide both regulation and management. The main task of a GTE control system is to provide the required performance while maintaining safe and stable operation [2]. The safetyconsiderationsincludethe flowinstability,highheatloads,and high cyclic stresses. In other words, the GTE control requirements include amainfuel controlforsetting andholdingsteady-state thrust (steady-state control mode) and fuel acceleration and deceleration schedules to provide limit protection (physical limitation control mode) and rapid time response for gas turbine engine (transient control mode) [3]. AsurveyonGTEcontrolstrategiesshowsthatvariousfuelcontrol methods have been used for GTE fuel control in the last two decades [4–16]. These studies show that the complexity of the engine control comes from the need to operate the engine as close as possible to its limits. In other words, the design of the control system for a gas turbine aero engine results in a series of single input single output controllers. Many methods such as PID, LQ, LQG, and H1 are used as one of the single feedback control loops of the GTE control system. In this case, the design method is based on a selection approach between various control loops. This strategy is named “min-max selection strategy,” as the selection of the control loops is based on a min-max approach between transient control loops. Minmax approach is an industrial method for the design of the GTE fuel control system. However, in order to provide an improved engine performance as well as the engine protection against the physical limitations, one of the most common complexities of the min-max selection strategy is controller gain tuning. Taking the nonlinearity and switching nature of this control strategy into account, gradient-based optimization methods have weak performance for gain tuning. As a result, this problem requires a non gradient optimization technique on the basis of the evolutionary algorithms. In this paper, application of an evolutionary algorithm for optimization of the min-max fuel controller parameters is presented. For this purpose, the control requirements and constraints for a gas turbine aero engine are first explained. The min-max fuel control strategy is then described and an initial min-max fuel controller is designed. Subsequently, using the genetic algorithm (GA), the parameters of the initial min-max fuel controller is tuned so that the engine requirements and constraints are satisfied. In optimization process, the fitness function is defined to minimize the settling time andfuelconsumption,wheretheweightfactorsaresetwithrespectto the importance of each function. In addition, a computer simulation programisdevelopedtoinvestigatetheeffectivenessoftheapproach for a single-spool jet engine. The results of simulation are validated by experimental data in both steady-state and transient modes to support the simulation model. Finally, the results are provided to investigate the performance of the optimized controller and to evaluate the effectiveness of the approach.