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Predictive Control Strategy in Distributed Networked Control Systems for Turbine Engine Under Faulty Communication Network

机译:通信网络故障下涡轮发动机分布式网络控制系统的预测控制策略

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Future turbine engines will require more efficient consumption of energy and greater reliability. A reduction in weight of turbine engine control systems and increased robustness will be critical in achieving said requirements. This paper presents the development of an advanced control strategy to replace the Full Authority Digital Electronic Control (FADEC) system (commonly used by turbine engines) with a lighter weight, Distributed Networked Control System (DNCS) that uses smart nodes (SN) architecture to enhance robustness. The concept of Distributed Networked Control Systems (DNCS) is rooted in the idea that the sum of the parts can be designed to weigh less than the whole, thereby allowing a single control system to be replaced by a set of sub-controller components that interact effectively through a network with improved functionality. The addition of artificial intelligent techniques employed to overcome the challenges inherent in DNCS operating under faulty communication networks are included. Specifically, an Artificial Neural Fuzzy Inference System (ANFIS) is employed to function as a state estimator within the distributed control nodes. The full implementation of the developed DNCS consists of distributed controllers, state estimators (ANFIS), and a simulated network interface. The focus of this paper is to report the development and test results related to the implementation of the described advanced distributed control methodology and its influence in recovering the engine operation in the presence of faults occurring within the communication network. The complete DNCS was tested on the MAPSS turbine engine simulation model. The test results showed that the developed DNCS with ANFIS state estimators improved turbine engine performance even under severe network delay conditions. As a result, the developed control system proved to be a viable alternative to the current engine control system. The research demonstrates that DNCS technology yields a reduction in engine weight leading to a reduction in energy consumption and a corresponding increase in engine efficiency and performance.
机译:未来的涡轮发动机将需要更有效的能源消耗和更高的可靠性。降低涡轮发动机控制系统的重量和提高坚固性对于实现上述要求至关重要。本文介绍了一种高级控制策略的开发,该策略将以重量轻,使用智能节点(SN)架构的分布式网络控制系统(DNCS)代替完全授权数字电子控制(FADEC)系统(通常由涡轮发动机使用)。增强鲁棒性。分布式网络控制系统(DNCS)的概念植根于这样的思想:零件的总和可以设计成重量小于整体重量,从而允许将单个控制系统替换为一组相互作用的子控制器组件。通过具有改进功能的网络有效地进行。其中包括为克服DNCS在故障通信网络下运行所固有的挑战而采用的人工智能技术。具体而言,采用了人工神经模糊推理系统(ANFIS)作为分布式控制节点内的状态估计器。所开发的DNCS的完整实现包括分布式控制器,状态估计器(ANFIS)和模拟的网络接口。本文的重点是报告与所描述的高级分布式控制方法的实施有关的开发和测试结果,以及在通信网络内出现故障时,该方法对恢复发动机运行的影响。完整的DNCS在MAPSS涡轮发动机仿真模型上进行了测试。测试结果表明,即使在严重的网络延迟条件下,带有ANFIS状态估计器的已开发DNCS仍可改善涡轮发动机的性能。结果,开发的控制系统被证明是当前发动机控制系统的可行替代方案。研究表明,DNCS技术可减轻发动机重量,从而降低能耗,并相应提高发动机效率和性能。

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