Analysis of space-filling excitation signals and dynamic models for nonlinear system identification of dynamic processes of a Diesel engine

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Volker Smits , Christopher Illg , Hermann Klein , Oliver Nelles
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引用次数: 0

Abstract

The paper investigates the influence of design of experiments (DoE) methods in combination with various mathematical architectures for the goal of an accurate Diesel engine identification. Three step-based excitation signals – amplitude-modulated pseudo random binary signal (APRBS), optimized nonlinear input signal (OMNIPUS), and the stacked global optimized amplitude time signal (sGOATS) – are analyzed regarding their space-filling property, a good excitation of all frequencies, and achievable model quality. Six different model architectures are used: a finite impulse response multilayer perceptron (MLP-NFIR), a long short-term memory (LSTM) network, a gated recurrent unit (GRU) network, a local model network with regularized finite impulse response models (LMN-NRFIR), a local model network with auto-regressive with exogenous inputs models (LMN-NARX), and a local model state space network (LMSSN). The model architectures are designed to be suitable for low-performance microcontrollers. As a process, a real-world system (Diesel engine) with high system complexity due to multiple feedback paths, strong nonlinearities, and infeasible regions is chosen, which allows a proper investigation of the data-based methods. The sGOATS achieves the best space-filling property, an even excitation of all frequencies, and provides good model qualities. It is shown that it is worth to optimize the excitation signals in order to get better models. Regarding the model architectures, the LMN-NARX and LMN-NRFIR perform worst, whereas the LSTM, GRU, and LMSSN slightly surpass the MLP-NFIR.
柴油机动力过程非线性系统辨识的充空激励信号分析及动力学模型
本文研究了实验设计方法结合各种数学体系结构对柴油机精确辨识的影响。分析了调幅伪随机二值信号(APRBS)、优化非线性输入信号(OMNIPUS)和叠加全局优化振幅时间信号(sGOATS)三种基于阶跃的激励信号的空间填充特性、全频率的良好激励和可实现的模型质量。使用了六种不同的模型架构:有限脉冲响应多层感知器(MLP-NFIR)、长短期记忆(LSTM)网络、门控循环单元(GRU)网络、正则化有限脉冲响应模型的局部模型网络(LMN-NRFIR)、带有外生输入模型的自回归局部模型网络(LMN-NARX)和局部模型状态空间网络(LMSSN)。该模型架构被设计为适用于低性能微控制器。作为一个过程,实际系统(柴油机)由于多反馈路径、强非线性和不可行的区域而具有高系统复杂性,从而允许对基于数据的方法进行适当的研究。sgoat实现了最佳的空间填充特性,所有频率的均匀激励,并提供了良好的模型质量。结果表明,为了得到更好的模型,对激励信号进行优化是值得的。在模型结构方面,LMN-NARX和LMN-NRFIR表现最差,而LSTM、GRU和LMSSN略优于MLP-NFIR。
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来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
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