Nonlinear Block-oriented Models Research Articles

Acceleration signal simulation induced by single obstacle crossings by means of nonlinear, block-oriented models for an automotive comfort simulator

This paper presents a method for replicating transient signals induced in a passenger car by single-obstacle crossings. These replicated signals should be used in a ride comfort simulator. By using feedforward-inverse-model-control with direct identification of the inverse transfer function, time-consuming iterations of conventional time waveform replication methods are eliminated. The quality of the system identification for approximating the inverse transfer function is significantly increased by decomposing the frequency band and the complexity is considerably reduced. The use of nonlinear block-oriented models significantly decreases the replication errors in frequency bands with high nonlinearity influence.

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area.

Modeling of low density polyethylene tubular reactor using nonlinear block-oriented model

Abstract In this paper, nonlinear block-oriented models, namely Neural Wiener (NW) and Neural Hammerstein (NH) model, are developed to simulate Low density polyethylene (LDPE) tubular reactor process. The desired simulated outputs of the reactor model are LDPE polymer conversion and melt flow index (MFI). Generally, a nonlinear block-oriented model consists of dynamic linear block and static nonlinear element, which are identified using nonlinear optimization techniques. A modified Neural Wiener (M−NW) with additional input scheme is also considered during the model development stage. In order to generate data for the model identification process, a steady state model of the LDPE polymerization process is developed using Aspen Plus and then converted into dynamic state using Aspen Dynamic software. Based on the model validation results, M−NW has outperformed the other two models with the coefficient of determination (R2) of 0.993 for polymer conversion and 0.986 for MFI results. Moreover, the simulation results for NW and NH models have shown a comparable performance in modeling the polymer conversion. However, the NW model has performed better in predicting MFI with R2 0.984 compared to than the NH model with R2 0.955. Thus, based on the simulation results, the application of nonlinear block-oriented models such as the M−NW and NW model in simulating the LDPE polymerization process is well justified. Such models can be further implemented in model-based control scheme or as soft sensor.

Volterra series identification and its applications in structural identification of nonlinear block-oriented systems

This paper considers the identification of a Volterra system and its applications in structural identification of nonlinear block-oriented models. Any order of the Volterra output is estimated separately via multilevel excitations and optimizations. Then, each order of the Volterra kernels is estimated independently with improved accuracy. Finally, relationships between the first and the second order Volterra kernel functions of block-oriented models are exploited to determine the structures of nonlinear block-oriented systems. The simulation studies verify the effectiveness of the proposed Volterra series identification method and the structure identification method for nonlinear block-oriented systems.

A Nonlinear Block-oriented Model for Wind tunnel System

This paper develops a novel nonlinear block-oriented model for the wind tunnel system. Based on the available signals, the wind tunnel system can be divided into three parts, namely, the exhaust valve loop, the choke finger loop and the flow field. Then the considered plant is described as a nonlinear block-oriented model. The exhaust valve subsystem and the flow field subsystem are both expressed by linear dynamic models, whereas the choke finger subsystem exhibits a nonlinear characteristics and is approximated by a pseudo-Hammerstein model. Based on the above parameterization model, the recursive identification algorithms are presented for three subsystems. Interestingly, the adaptive weighted recursive least squares algorithm is applied to the pseudo-Hammerstein model, and the hierarchical recursive least squares algorithm is used to reduce the computational complexities. Both simulations and experiments are carried out to verify the effectiveness of the proposed method.DOI: http://dx.doi.org/10.5755/j01.itc.46.3.14457

Structure Detection of Wiener–Hammerstein Systems With Process Noise

Identification of nonlinear block-oriented models has been extensively studied. The presence of the process noise, more precisely its location in the block-oriented model influences essentially the development of a consistent identification algorithm. The present work is proposed with the aim to localize the process noise in the block-oriented model for accurate nonlinear modeling. To this end, the response of a Wiener-Hammerstein system is theoretically analyzed, the disturbance component in the output, caused by the process noise preceding the static nonlinearity, is shown to be dependent on the input signal. Inspired by such theoretical observation, a simple and new protocol is developed to determine the location of the process noise with respect to the static nonlinearity by using an input signal that is periodic, but nonstationary within one period. In addition, the proposed technique is promising to detect the type of certain static nonlinearity (e.g., dead-zone, saturation). Finally, it is validated on a simulated example and a real-life benchmark.

Structural Identification of Nonlinear Dynamic Objects in the Conditions of a Passive Experiment

A structural identification algorithm for nonlinear block-oriented models of the Hammerstein type and the Zadeh filter is considered. The algorithm for structural identification relies on the narrow-band probing of a nonlinear object to the input of which test signals cannot be fed. Frequency-time criteria are listed, with the aid of which the structures of adequate models are defined.

Formation of an artificial spectrum with a fraunhofer line

In this paper, nonlinear block-oriented models, namely Neural Wiener (NW) and Neural Hammerstein (NH) model, are developed to simulate Low density polyethylene (LDPE) tubular reactor process. The desired simulated outputs of the reactor model are LDPE polymer conversion and melt flow index (MFI). Generally, a nonlinear block-oriented model consists of dynamic linear block and static nonlinear element, which are identified using nonlinear optimization techniques. A modified Neural Wiener (M−NW) with additional input scheme is also considered during the model development stage. In order to generate data for the model identification process, a steady state model of the LDPE polymerization process is developed using Aspen Plus and then converted into dynamic state using Aspen Dynamic software. Based on the model validation results, M−NW has outperformed the other two models with the coefficient of determination (R2) of 0.993 for polymer conversion and 0.986 for MFI results. Moreover, the simulation results for NW and NH models have shown a comparable performance in modeling the polymer conversion. However, the NW model has performed better in predicting MFI with R2 0.984 compared to than the NH model with R2 0.955. Thus, based on the simulation results, the application of nonlinear block-oriented models such as the M−NW and NW model in simulating the LDPE polymerization process is well justified. Such models can be further implemented in model-based control scheme or as soft sensor.