System Identification Methodology Research Articles

Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries

Precisely characterizing Li-ion batteries is essential for optimizing their performance, enhancing safety, and prolonging their lifespan across various applications, such as electric vehicles and renewable energy systems. This article introduces an innovative nonlinear methodology for system identification of a Li-ion battery, employing a nonlinear autoregressive with exogenous inputs (NARX) model. The proposed approach integrates the benefits of nonlinear modeling with the adaptability of the NARX structure, facilitating a more comprehensive representation of the intricate electrochemical processes within the battery. Experimental data collected from a Li-ion battery operating under diverse scenarios are employed to validate the effectiveness of the proposed methodology. The identified NARX model exhibits superior accuracy in predicting the battery's behavior compared to traditional linear models. This study underscores the importance of accounting for nonlinearities in battery modeling, providing insights into the intricate relationships between state-of-charge, voltage, and current under dynamic conditions.

A practical solution for multivariable control of temperature and humidity in greenhouses

This paper presents a simple and effective control solution to regulate temperature and humidity inside a greenhouse, which are the main climatic variables affecting crop growth and health. A multivariable control strategy with inverted decoupling is proposed in two schemes. The nighttime control scheme uses heating and dehumidification, and the daytime control scheme uses natural ventilation, dehumidification, and humidification. A system identification methodology is used to obtain low-order linear models from data measured in a greenhouse. This simplifies the design of proportional–integral (PI) controllers and allows the use of well-known control techniques. For instance, anti-windup is used to deal with the saturation of the actuators and for a bumpless transfer mechanism to switch between the nighttime and the daytime control schemes. Setpoint weighting is used for the humidity controllers to attenuate undesired effects on the temperature control loop when the humidity setpoint is changed. Furthermore, split range control is used for the first time to control humidity by combining a heat-pump dehumidifier and a fogging system. The control solution presented in this work allows the integral absolute error to be reduced, on average, by 88.5% for the interactions affecting the temperature control loop, and by 85.6% for the humidity control loop, compared to decentralized PI control. The control schemes have been tested in simulation with real disturbance data from a Mediterranean greenhouse and in preliminary form in a real facility to conclude that the proposed multivariable control strategy could be effective in decoupling the temperature and the humidity control loops.

Experimental linear parameter-varying model identification of an elastic kinetic roof structure

Elastic kinetic structures are a recent approach to design transformable lightweight structures. Their transformation is based on elastic bending, exploiting the compliant material behavior of the structural members. This enables transformable structures with a stable transformation process. However, due to their lightweight and flexible design, elastic kinetic structures are highly sensitive to static and dynamic disturbances. Moreover, such transformable structures exhibit a transformation state dependent dynamic behavior, due to geometric nonlinearities. Nevertheless, most of current research focuses on the principles of elastic kinetic transformation than on effective disturbance mitigation. The latter is usually designed based on low-order control-oriented models. This paper describes a system identification methodology, suitable to identify low-order models for the transformation state dependent dynamics of elastic kinetic structures. The system identification is carried out using a local approach in the linear parameter-varying (LPV) framework. For the identification of local linear time-invariant (LTI) models, the system realization using the information matrix (SRIM) method is applied. The collection of local LTI models is internally balanced using a balancing transformation, in order to represent the locally estimated models with respect to a common state–space basis. Via linear interpolation, an LPV system in grid-based representation is derived. This methodology is experimentally validated with data from an active hybrid roof structure prototype.

Humidity Control for Air Circulation in the Drying Process

Recycling exhaust air is acknowledged as a method to reduce the energy consumption of agricultural products in the dryer. This study investigates the performance of an air circulation system at a laboratory scale and develops a feedback control compensator for optimizing the drying air circulation process. A servo motor is employed to drive a valve, to feed the exhaust drying air with high temperature and humidity back in different proportions. The system is controlled using an Arduino DUE microcontroller, which communicates data with MATLAB/Simulink. The system identification methodology is employed to analyze the mathematical model of the system. The result indicates that the response of the system meets the acceptance criteria when the percent overshoot is less than 25%, and the settling time is within 60 seconds (with a 2% error tolerance). Evaluation of control system performance during equilibrium employs R2 and RMSE values.

A Novel Parameter Estimation Scheme for Vehicle Suspension Systems Based on Response and Test Track Prioritization

In this paper, a system identification methodology based on vehicle response and test track prioritization is presented. The proposed method can be used to perform time-domain parameter estimation by simply driving the vehicle on different road profiles, thus eliminating the necessity of having a special test setup or excitation input feed. The method is based on exploiting the dependence of the identifiability of vehicle parameters on vehicle responses and test tracks. To prioritize the test tracks, the responses and test tracks are weighed based on their capabilities to estimate the vehicle parameters, and these weights are obtained in a preliminary analysis of a seven-degree-of-freedom ride model developed in MATLAB® (R2021b). An optimization framework based on a differential evolution scheme with an optional archive scheme (JADE) is used to estimate the twelve vehicle parameters. The proposed system identification scheme is verified on tests using a high-fidelity vehicle model developed in ADAMS® (29.2)using several tracks with different severities. Experimental validation is performed on a test vehicle, and it is demonstrated that the vehicle suspension system parameters can be identified accurately with fast convergence performance.

Automated multi-objective system identification using grammar-based genetic programming

In order to use existing identification tools effectively, a user must make critical choices a priori that ultimately determine the quality of estimated models. Furthermore, while estimated models are typically optimized for a single identification criterion, engineering applications typically impose multiple performance specifications that may contradict each other. In this contribution, we develop a system identification methodology that automatically selects parametric model structures from a wide range of dynamic system models based on measured data. The problem of inferring model structures and estimating model parameters within these structures is encapsulated in a bi-level optimization problem. The optimization problem is formulated for multiple user-specified identification objectives. Finally, the range of dynamical systems considered for the optimization problem is specified using Tree Adjoining Grammar. A solution approach based on genetic programming is developed, and its asymptotic properties and computational complexity is analysed. The empirical performance of the proposed identification techniques is studied using a simulation example.

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks.

The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10-5, RMSE of 0.4411% and a MAPE of 0.0643.

Representative linearised models for a wave energy converter using various levels of force excitation

In guiding the progression, development, and operation of wave energy converters (WECs) in a more efficient way, mathematical analysis and understanding of the dynamic process is essential. Mathematical WEC models, obtained either by numerical analysis or physical modelling, form the basis of most (model-based) energy maximising control strategies available in the literature, where experimental design and system identification methodology directly impact the resulting model. This study, using an experimental-based WEC model (which can be used for linear control design), investigates the dynamic behaviour of a WEC by analysing the dominant poles of the system, generated using fully nonlinear computational fluid dynamics (CFD)-based numerical wave tank (NWT) experiments. The aim is to effectively track the dominant dynamics of the WEC, using different force-input amplitude levels in the NWT setup, and perform a comparison with the classical linear boundary-element-methods (BEM) equivalent methodology. Thus, the presented case studies are shown to agree with previously proposed model assessment of linear WEC models, based on a free-decay NWT setup. In addition, the representative WEC models determined as part of this study can be used for WEC controller design, either singly, or using a form of model/controller gain scheduling.

Multivariable control of nighttime temperature and humidity in greenhouses combining heating and dehumidification

This work presents a multivariable control strategy to regulate nighttime temperature and relative humidity inside a greenhouse, using a pipe heating system and a dehumidification system. The greenhouse is modeled as a two-input two-output (TITO) process with a system identification methodology to calculate low-order linear models. Proportional-integral (PI) controllers with anti-windup are used, and an inverted decoupling scheme is adopted. The strategy was tested in simulation with real data. Results suggest that it is attractive for real implementation and show for the first time that the use of dehumidification systems can be applied as part of a combined control strategy.

Performance Analysis of Controller for Non-Linear System using Model Based and Data Driven Techniques

The main challenge for every sector is regulating the flow and level of the liquid during the process. In various manufacturing sectors, the conical tank is frequently employed. The conical tank's design causes non-linearity, therefore controlling the liquid level in a conical tank process is the most difficult issue. As a result, the process output may occasionally be impacted. The level control of the conical process has been the subject of a great deal of research. A conical bottomed tank is utilized for complete fluid drainage in various process industries, where its nonlinearity may only be at the bottom. The tank can be made entirely conical to increase drainage effectiveness even further. However, because of the tank's constant change, the process is highly nonlinear, making it challenging to manage the liquid level. Most commonly, a conical-shaped tank system is used in colloidal mills, leaching extractions in the chemical and pharmaceutical industries, food processing industries, petroleum industries, molasses, liquid feed and liquid fertilizer storage, chemical holding and mix tanks, and reactor tanks for the processing of biodiesel. Conical tanks are employed in storage and holding tanks to prevent settlement and sludge. On the basis of traditional modelling (transfer function modelling), system identification and data-based methodology, modelling and controller design are carried out on a single conical tank. Based on time domain specifications, the performance of the designed controllers is examined. Data-based controller design has been shown to be superior to the other two strategies. A controller is designed and then modelled using the MATLAB platform.

A metaheuristic-based methodology for efficient system identification of the Proton Exchange Membrane Fuel Cell stacks

In this study, a new procedure for optimum and effective system identification of Proton Exchange Membrane Fuel Cell (PEMFC) stacks is introduced. The present study proposes a method to obtain the optimum amount of the uncertain parameters of the PEMFC design to deliver the uppermost confirmation with the real output voltage value. To do so, the integral of the absolute error (IAE) between the real and model outputted voltage values is considered an objective function. To minimize this function, a modified design of the emperor penguin optimizer has been suggested. The method has been then investigated by performing two real case study models and its achievements are put in comparison with several latest techniques to show the method's productivity. The achievements show that the suggested technique has a higher ability for parameter estimation of the PEMFC models.

Ship Air-Wake Identification from Experimental Data for Automatic Deck Landing and Takeoff

Ship air-wake modeling is a critical task needed to support the design and validation of algorithms that can assist a helicopter’s pilot during shipboard launch and recovery operations. In fact, these operations are often carried out in challenging conditions and impose a significant workload on the pilot. In this framework, the paper presents an air-wake model applicable for flight mechanics analyses and real-time simulations. The definition of the model is based on system identification methodologies applied to wind-tunnel experimental data. The main innovation of the approach consists of the definition of a modular structure of the model that allows setting up a multistep identification strategy and exploiting the most suitable technique for the estimation of each set of the model’s parameters. The validation of the obtained model highlighted a good capability to reproduce the air-wake flowfield in several flow conditions. The model was applied to test trajectory generation and tracking algorithms for helicopter automatic takeoff and landing on a ship deck.

Control-Enabling Adaptive Nonlinear System Identification

In this article, we rely on the theoretical foundations of radial basis function neural networks to form an adaptive parameter estimation problem, which we solve using the recently introduced prescribed performance control methodology. Such combination results in a compact user-configurable adaptive nonlinear system identification methodology that can be used to retrieve the open-loop nonlinear plant dynamics in any compact region of interest.

Variable Speed Controller of Wind Generation System using Model predictive Control and NARMA Controller

This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller’s design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.

Data-driven modeling of transonic unsteady flows and efficient analysis of fluid–structure stability

The reduced-order aerodynamic models constructed via the linear/nonlinear system identification methodologies cannot reveal the flow characteristics of the fluid–structure coupling system because the state variables of the reduced-order model do not explicitly represent fluidic properties. In this study, a data-driven modeling procedure is proposed to reconstruct a physics-based, reduced-order aerodynamic model. In the procedure, the transonic unsteady flows of concern are projected onto low-dimensional base vectors first via the proper orthogonal decomposition of pressure snapshots subject to a specific structural excitation. Then a state-space representation of the temporal coefficients of proper orthogonal decomposition modes subject to the structural excitation is established by using the dynamic mode decomposition with control. Finally, for the fluid–structure stability analysis, pressure snapshots are recovered from the coefficients of proper orthogonal decomposition, and aerodynamic forces are derived by integrating the pressure coefficients around the wing surface. The state vector in above-mentioned data-driven model has a clear sense in physics with regard to pressure distribution. To demonstrate the accuracy of the proposed procedure, a two-dimensional, transonic aeroelastic wing with an NACA0012 profile is studied. The unsteady aerodynamic forces, frequency responses of the reduced-order aerodynamic model, transonic flutter boundary, and flow characteristics at the flutter condition are predicted and compared with direct computational fluid dynamic simulations. The results show that the modeling procedure can accurately predict the transonic flutter boundary and flow characteristics.

Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms

In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure’s modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field.This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm’s parameters need to be constantly updated by the user.

Study of nonlinear effects in a bolted joint using the base excitation as an input

In this paper, the nonlinearity detection, characterization and identification of a bolted beam assembly is presented. The methodology utilized in this work is based on the force reconstruction using the base excitation as an input and the nonlinear system identification. The force reconstruction procedure was carried out by exciting the bolted beam assembly at very low excitation with fully tightened bolt condition. The nonlinear effect in the bolted beam assembly was induced by reducing the bolt clamping loads. A collection of frequency response functions (FRFs) are shown at different clamping loads to detect and characterize the nonlinearities. Once the nonlinearities are detected and characterized, the restoring force surface method using the reconstructed force was used to identify the nonlinear parameters in the modal space. Four different base excitation (energy) levels with three different tightening torques were considered in the tests in order to study the energy dependence of the damping nonlinearities. In all the cases, the nonlinear system identification methodology employed was successful in identifying the damping and stiffness nonlinearities.

Polynomial and transfer function models for multi input single output (MISO) PEM fuel cell by system identification methodology

Mathematical modelling is the tool to reveal and fully understand the input output relationship in most of the dynamic systems including fuel cells. The dynamic behaviour of fuel cell system is determined by a set of system processes and every process is characterised by its physical, mechanical or chemical properties, which are not readily known. A fuel cell system model always tries to find important aspects related to its dynamic behaviour. Due to the complex internal structure of Polymer Electrolyte Membrane (PEM) fuel cell, it is not so easy to set its internal and external parameters for its optimum performance and hence an advance modelling method need to be apply to analyse and optimise its dynamic performance. In this paper, most simplified autoregressive polynomial and transfer function models for a multi-input single output PEM fuel cell is presented using the system identification black box methodology. Simulated data of both the developed models are validated with the experimentally measured data from a practical PEM fuel cell. The accuracy of both the developed models is more than 95% and therefore the developed models can be used to analyse the performance of PEM fuel cell at any loading condition.

Numerical Assessment of Polynomial Nonlinear State-Space and Nonlinear-Mode Models for Near-Resonant Vibrations

In the present article, we follow up our recent work on the experimental assessment of two data-driven nonlinear system identification methodologies. The first methodology constructs a single nonlinear-mode model from periodic vibration data obtained under phase-controlled harmonic excitation. The second methodology constructs a state-space model with polynomial nonlinear terms from vibration data obtained under uncontrolled broadband random excitation. The conclusions drawn from our previous work (experimental) were limited by uncertainties inherent to the specimen, instrumentation, and signal processing. To avoid these uncertainties in the present work, we pursued a completely numerical approach based on synthetic measurement data obtained from simulated experiments. Three benchmarks are considered, which feature geometric, unilateral contact, and dry friction nonlinearity, respectively. As in our previous work, we assessed the prediction accuracy of the identified models with a focus on the regime near a particular resonance. This way, we confirmed our findings on the strengths and weaknesses of the two methodologies and derive several new findings: First, the state-space method struggles even for polynomial nonlinearities if the training data is chaotic. Second, the polynomial state-space models can reach high accuracy only in a rather limited range of vibration levels for systems with non-polynomial nonlinearities. Such cases demonstrate the sensitivity to training data inherent in the method, as model errors are inevitable here. Third, although the excitation does not perfectly isolate the nonlinear mode (exciter-structure interaction, uncontrolled higher harmonics, local instead of distributed excitation), the modal properties are identified with high accuracy.

Simple Tuning Rules for Feedforward Compensators Applied to Greenhouse Daytime Temperature Control Using Natural Ventilation

In this work, simple tuning rules for feedforward compensators were applied to design a control strategy to regulate the inside air temperature of a greenhouse during daytime by means of a natural ventilation system. The developed control strategy is based on a PI (Proportional-Integral) controller combined with feedforward compensators to improve the performance against measurable external disturbances such as outside air temperature, solar radiation, and wind velocity. Since the greenhouse process dynamics is very complex and physical non-linear models are mathematically complicated, a system identification methodology was proposed to obtain simpler models (high-order polynomial and low-order transfer functions). Thus, an easier procedure was completed to tune the PI controller parameters and to obtain the feedforward compensators expressions by following a series of modern and simple tuning rules. Simulations with real data were executed to compare the control performance of a PI controller with or without the addition of feedforward compensators. Moreover, real tests for the developed control strategy were carried out in an experimental greenhouse. Results demonstrate an enhanced control performance with the presence of the feedforward compensators under different weather conditions.