Experimental Input-output Data Research Articles

Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks

Traditional control and monitoring of water quality in drinking water distribution networks (WDNs) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (1) complex water quality and reaction dynamics; and (2) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification.

Effect of Tea Infusion Strength on Equivalent Circuit Parameters of Multielectrode Voltammetric Sensor System

Analysis of voltammetric response obtained from black tea liquor samples has been used extensively in research to evaluate tea quality. The recent system identification-based quality estimation method of black tea uses the equivalent circuit parameters of the voltammetric system. This work reports the analysis of these equivalent circuit parameters of a multielectrode voltammetric system in correlation with the change in concentration of black tea liquor. In this work, the experimental input–output data obtained using the voltammetric analysis of tea liquor is subjected to the system identification to obtain the system process model. The model hence obtained is subjected to a network synthesis procedure to estimate the equivalent circuit parameters of the system with various electrodes and at different concentrations of tea samples. Therefore, the tea infusion strength is simulated by varying concentrations. The performance parameters of the models identified for all the electrode–analyte combinations are calculated by comparing the experimental and the simulated waveforms. The study of the concentration of tea samples and circuit parameters agrees with the theoretical concepts. This work therefore also provides a legitimate validation to the established equivalent circuit for the voltammetric taste measurement system (electronic tongue). The cluster analysis performed here reveals a potential application of this work in the domain of electronic tongue.

Experimental Investigation of the Influence of Sand and Binder Composition on the Mold Properties of Alkyd Type No-Bake Chemically Bonded Sand-Casting System

In comparison to green sand moulds, chemically bonded resin sand moulds have better dimensional accuracy, surface quality, and sand mould qualities. To survive sand drops when pouring molten metal, the mould cavity formed using a chemically bonded sand mould technique must have appropriate permeability, strength, and hardness. The desire for better permeability, strength, and mould hardness is based on a thorough investigation and analysis of the affecting parameters, such as resin percentage, hardener, and catalyst. The influence of binder content on the moulding qualities of silica sand bound with Alkyd oil urethane binder was investigated. Using a sieve shaker, the experimental materials were sieved and manually blended with the binders. AFS standard test specimens (50 mm diameter by 50 mm height) were prepared using a sand rammer, and four key moulding parameters were determined using a universal sand strength machine, permeability meter, and mould hardness tester: green compression strength (GCS), green shear strength (GSS), permeability, and mould hardness. For the minimal experiments, Box-Behnken experimental matrices were used, and the statistical significance of influencing factors and their interactions will be identified to manage the process. To statistically validate the model, an analysis of variance (ANOVA) test was performed using Minitab. Mold hardness, strength, and permeability will each have their own mathematical equation, which was stated as a nonlinear function of input factors based on experimental input-output data. To optimize the process parameters, a response optimizer (using Minitab) has been used. The results revealed that increasing the resin concentration from 1% to 2% enhances permeability and GSS while decreasing GCS and mould hardness. Hardener was increased from 18 to 20%, which resulted in a drop in permeability and GSS but an increase in GCS and mould hardness. Similarly, increasing the catalyst concentration from 2% to 10% reduces permeability and mould hardness while increasing GCS and GSS.

Micro engraving on Ti-6Al-4V using fiber laser for orthopedic implant-A study

The demand for production of fine micro-grooves with least heat-affected zone (HAZ), recast layer thickness, microcrack, and microcavity are increasing as Ti6Al4V is a readily used orthopedic implant material. This study presents a groove quality optimiser called response surface methodology (RSM) for fiber laser micro engraving on Ti6Al4V to improve microgroove quality and functionality. The critical process factors’ (number of laser beam passes, scan speed, and pulse frequency) for each response characteristic are identified through the RSM optimiser by analysing the experimental data sets. The optimised data sets are beneficial in maintaining groove quality characteristics (width, depth, HAZ, recast layer thickness, and microhardness). Number of passes and scanning speed has been recognised as crucial process parameters that primarily regulate the groove response characteristics. A 3-20-5 neural network (NN) model is developed and trained through the experimental input–output data sets. Performance of 3-20-5 model is found as 1.63e-27, and regression score is significantly closer to 1. It has been recognised that ANN model predicts more accurate results with a low error percentage than RSM model even when the input factor level settings are beyond the defined boundary. Under these experimental settings, the microgroove characteristics are enhanced in terms of low HAZ, recast layer thickness, microhardness, and spatter. X-ray diffraction analysis (XRD) is also performed on base metal and grooved surfaces to determine diffraction peaks, crystallite phase composition, size, and lattice strain. XRD analysis revealed the presence of both HCP-α-Ti and BCC-β-Ti in both base metal grooved surfaces.

Hysteresis modelling and compensation for piezoelectric actuator using Jaya-BP neural network

This paper proposes a new training algorithm using a hybrid Jaya-back propagation algorithm (called H-Jaya) to optimize the neural network weights, which is applied to identify the nonlinear hysteresis Piezoelectric actuator based on the experimental input-output data. The identified H-Jaya-neural model will be used to design an advanced feed-forward (FF) controller for compensating the hysteresis nonlinearity. Furthermore as to improve the tracking performance, a feed-forward-feedback control scheme is conducted. To evaluate the effectiveness of the proposed approach, firstly, it is tested through identifying the nonlinear hysteresis of Piezoelectric (PZT) actuator and compared with other meta-heuristic techniques, including differential evolution (DE), particle swarm optimization (PSO), and Jaya. Then, the accuracy of the hysteresis model-based compensator is evaluated under various control experiments using the piezoelectric actuator. The results of experiments executed on PZT actuator configured with a PZS001 from Thorlabs prove that the proposed approach obtains an excellent performance in hysteresis modeling and compensation.

Grey-box model calibration and validation for a continuous horizontal fluidized bed dryer

Fully continuous drying can improve the production flexibility and throughput of tablets manufactured by wet granulation lines. A recently developed prototype incorporates a screw conveyor to a horizontal fluidized bed dryer for pharmaceutical particles. Research on advanced process control strategies led to the publication of a two-phase tanks-in-series framework to describe its dynamics. This paper calibrates the model to replicate the prototype behavior. A grey-box identification methodology estimates the empirical and uncertain parameters using experimental input-output data. The underlying assumptions are cross-validated with time-domain comparisons, output root mean square errors and confidence intervals. Results show that the model reproduces the observed trends in general and the discrepancies result from mechanical issues on the equipment.

Pareto Optimization of GMDH-Type Neural Networks for Modelling and Prediction of Hoop Strain in Explosive Forming Process

In this paper, Evolutionary Algorithms (EAs) are deployed for multi-objective Pareto optimal design of Group Method of Data Handling (GMDH)-type neural networks that have been used for modelling of the effect of dynamic yield stress, thickness, charge mass, centre deflection and distance from centre on the hoop strain in explosive forming process using some input-output experimental data. In this way, EAs with a new encoding scheme is firstly presented to evolutionary design of the generalized GMDH-type neural networks in which the connectivity configurations in such networks are not limited to adjacent layers. Multi-objective EAs (non–dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto optimization of such GMDH-type neural networks. The important conflicting objectives of GMDH-type neural networks that are considered in this work are, namely, Training Error (TE), Prediction Error (PE) and number of neurons (N) of such neural network. Different pairs of these objective functions are selected for 2-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case which exhibit the trade-offs between the corresponding pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural networks models for the explosive forming process. Moreover, all the three objectives are also considered in a 3-objective optimization process which consequently lead to some more non-dominated choices of GMDH-type models representing the trade-offs among the training error, prediction error, and number of neurons (complexity of network), simultaneously. The overlay graphs of these Pareto fronts also reveal that the 3-objective results include those of the 2-objective results and, thus, provide more optimal choices for the multi-objective design of GMDH-type neural networks in terms of minimum training error, minimum prediction error and minimum complexity.

Application of Artificial Neural Networks to Predict the Catalytic Pyrolysis of HDPE Using Non-Isothermal TGA Data.

This paper presents a comprehensive kinetic study of the catalytic pyrolysis of high-density polyethylene (HDPE) utilizing thermogravimetric analysis (TGA) data. Nine runs with different catalyst (HZSM-5) to polymer mass ratios (0.5, 0.77, and 1.0) were performed at different heating rates (5, 10, and 15 K/min) under nitrogen over the temperature range 303–973 K. Thermograms showed clearly that there was only one main reaction region for the catalytic cracking of HDPE. In addition, while thermogravimetric analysis (TGA) data were shifted towards higher temperatures as the heating rate increased, they were shifted towards lower temperatures and polymer started to degrade at lower temperatures when the catalyst was used. Furthermore, the activation energy of the catalytic pyrolysis of HDPE was obtained using three isoconversional (model-free) models and two non-isoconversional (model-fitting) models. Moreover, a set of 900 input-output experimental TGA data has been predicted by a highly efficient developed artificial neural network (ANN) model. Results showed a very good agreement between the ANN-predicted and experimental values (R2 > 0.999). Besides, A highly-efficient performance of the developed model has been reported for new input data as well.

Methodology for modeling fuzzy Kalman filters of minimum realization from evolving clustering of experimental data

A methodology for evolving fuzzy Kalman filter identification, is proposed in this paper. The mathematical formulation contemplates the following aspects: for initial estimation, an offline GK clustering algorithm and an offline fuzzy version of OKID algorithm are used to estimate antecedent and consequent parameters, respectively. From each new sample of input–output experimental data from dynamical system, the evolving eTS algorithm and an evolving fuzzy version of OKID algorithm are used to estimate the antecedent and consequent parameters of the evolving fuzzy Kalman filter, respectively. Computational and experimental results considering the estimation of states and outputs of a nonlinear dynamic system and a 2DoF helicopter, respectively, show the efficiency and applicability of the proposed methodology.

Experimental analysis and prediction of strength of adhesive-bonded single-lap composite joints: Taguchi and artificial neural network approaches

Adhesive-bonded joints made up of composite materials offer complex structures with the ease of joining similar or dissimilar materials. The failure behavior of adhesive-bonded joints is influenced largely by geometrical parameters (overlap length and adhesive thickness) and adherend surface preparations. The glass fiber-reinforced epoxy composites are prepared for single-lap adhesive joints to know their strength with different sets of geometrical factors. The adherend surfaces of composites are roughened to 2 ± 0.1 µm, prior to joint preparation. Taguchi L9 experimental matrix representing different combinations of overlap length and adhesive thickness is employed to know the behavior of failure load (FL) and shear strengths (SS) of the adhesive-bonded single-lap composite joints. The results showed that the impact of overlap length of adhesive-bonded joints is more compared to that of adhesive thickness. The interaction effects of geometrical parameters are found negligible toward both the outputs. The optimal factor levels received the highest load to fracture; the joints are found equal to 6096.1 N for FL and 80 MPa for SS, respectively. The empirical relationship based on multiple linear regression (MLR) equations is derived for both the failure load and shear strength. Levenberg–Marquardt algorithm-trained neural networks (NNs) are used for the prediction of both responses. Ten experimental cases are used to check the prediction capabilities of both MLR and NNs. The mean absolute percent error in prediction of both the responses is found equal to 2.27% for NNs and 3.12% for MLR. The NNs and MLR results in accurate prediction might be due to the model development process based on experimental input–output data, rather than assumption-based theoretical and numerical methods.

Hybrid controller for precision positioning application

In this paper, a precision positioning hybrid controller for long travel distance with submicron/nanometer resolution and accuracy is designed and developed. A hybrid control algorithm is designed to combine the coarse positioning system and a precision positioning system. The coarse positioning system consists of a linear stepper motor and coarse positioning controller, which is designed by microstepping with proportional-integral (PI) current feedback (MSPICF) control. In the precision positioning system, a piezoelectric actuator (PZA) is used. The mathematical model of the PZA has been represented by 2nd order mass-spring system with the Dahl hysteresis model and the model parameters are estimated by an autoregressive with exogenous terms (ARX) model identification technique using the input–output experimental data. The precision positioning controller designed by feedforward (FF) control which is the inverse of the mathematical model of the PZA and feedback (FB) control. The coarse, precision and hybrid controller is implemented using a low-cost DsPIC30F4012 microcontroller. Experiments have been performed to evaluate the performance of the controller.

Multi-Point Search Based Identification under Severe Numerical Conditions

It is necessary to perform the system identification under severe numerical conditions in many practical applications. When less external test signals are available for parameter estimation from experimental data, the identification performance often suffers from numerical problems in the optimization procedure due to the less independent informative components, the influence of complicated noise, or the local minima problem. In this paper, a multi-point search based identification algorithm is investigated for system identification under severe numerical conditions. It introduces the output over-sampling scheme to collect the experimental input-output data, and extracts the information in time and space domains to complement information criterion for numerical optimization. Furthermore, the multi-point search is utilized to decrease the influence of local minima. The numerical simulation examples illustrate that the identification performance has been improved in the proposed algorithm.

Experimental study of structural change detection using data-driven reduced-order models

This paper presents a comparison of the effectiveness of three different data-driven vibration-based approaches in detecting and locating structural changes in a 1/4 scale six-storey single-bay steel frame laboratory structure from measured experimental input-output data obtained from band-limited white-noise base-excitation tests. The implemented methodologies are based on reduced-order models obtained using three input-output system identification approaches: a system realisation algorithm using information matrices, a general time-domain least-squares identification method, and a non-parametric chain-like system identification approach. Variations in the estimated reduced-order models are then used to indicate the presence and infer the location of actual structural changes in the test structure. The results of this experimental study show that even though the changes introduced by the various levels of damage in the structure were robustly detected in the presence of modelling, measurement, and data processing errors using reduced-order representations, the identified change locations in the reduced-order model could not be, in some cases, reliably correlated with the actual damage location in the structure.

Parameters identification of Bouc–Wen hysteresis model for piezoelectric actuators using hybrid adaptive differential evolution and Jaya algorithm

The nonlinearities hysteresis of the piezoelectric-PZT actuator can greatly degrade in precise positioning applications. Therefore, modeling and identifying the hysteresis parameter of PZT actuator are still many challenges nowadays. In this paper, the hybrid adaptive differential evolution and Jaya algorithm (aDE-Jaya) is proposed to identify the Bouc–Wen hysteresis model of a piezoelectric actuator. In the aDE-Jaya algorithm, the improvement is focused on a hybrid mutant operator “DE/rand/1” and Jaya operator tried to balance between two contradictory aspects of their performance: exploration and exploitation and adaptive control parameters (mutant factor F, crossover rate CR, population size NP) to enhance the convergence efficiency. To prove the effectiveness and robustness of the proposed aDE-Jaya algorithm, it is tested on 8 benchmark functions and compared with other state-of-the-art optimizations. The comparison results show that aDE-Jaya has better performance in convergence rate and accuracy. After that, aDE-Jaya is applied to identify the Bouc–Wen hysteresis model based on experimental input–output data. The identified Bouc–Wen hysteresis resulted is used to design the feedforward controller to test accurate identification. As a consequent, the proposed aDE-Jaya algorithm can successfully identify the highly hysteretic nonlinearity of the piezoelectric actuator with perfect precision.

Controller Design and Stability Analysis of Output Pressure Regulation in Electrohydrostatic Actuators

In this paper, a robust fixed-gain linear output pressure controller is designed for a double-rod electrohydrostatic actuator using quantitative feedback theory (QFT). First, the family of frequency responses of the system is identified by applying an advanced form of fast Fourier transform on the open-loop input–output experimental data. This approach results in realistic frequency responses of the system, which prevents the generation of unnecessary large QFT templates, and consequently contributes to the design of a low-order QFT controller. The designed controller provides desired transient responses, desired tracking bandwidth, robust stability, and disturbance rejection for the closed-loop system. Experimental results confirm the desired performance met by the QFT controller. Then, the nonlinear stability of the closed-loop system is analyzed considering the friction and leakage, and in the presence of parametric uncertainties. For this analysis, Takagi–Sugeno (T–S) fuzzy modeling and its stability theory are employed. The T–S fuzzy model is derived for the closed-loop system and the stability conditions are presented as linear matrix inequalities (LMIs). LMIs are found feasible and thus the stability of the closed-loop system is proven for a wide range of parametric uncertainties and in the presence of friction and leakages.

Adaptive feed-forward controller of piezoelectric actuator for micro/nano-positioning

In this paper, design and implementation of an adaptive feed-forward controller for micro/nano-positioning control of piezoelectric actuator (PEA) is described. Discrete-time Dahl hysteresis-based mathematical model of the PEA is developed and the values of the model parameters are estimated through an autoregressive with exogenous terms (ARX) model identification technique using experimental input–output data. A recursive least-square estimator (RLSE)-based adaptive feed-forward (FF) controller is proposed, which takes into account the parameter uncertainty. The FF controller has also been implemented in a DsPIC30F4011 microcontroller. The established PEA model and the controller are validated by simulation and experimental results including parameter variation.

Hysteresis modelling and feedforward compensation of piezoelectric nanopositioning stage with a modified Bouc‐Wen model

Piezoelectric actuators (PEAs) are widely applied in various nanopositioning equipment. However, the strong hysteresis nonlinearity compromises the positioning accuracy. In this work, a novel modified Bouc-Wen (MBW) model with a polynomial function of the differential of the input is established for modelling the hysteresis nonlinearity of the PEA-actuated nanopositioning stages. The particle swarm optimisation algorithm is adopted to identify the parameters of the MBW model with a set of input-output experimental data. The obtained model with the corresponding identification parameters matches well the experimental data with 0.31% relative error. A feedforward compensator based on the obtained model is also applied to compensate the hysteresis nonlinearity. Experiments are conducted to validate the effectiveness of this approach, and the results show the great improvement of positioning accuracy of the stage.

Closed-loop parametric identification of DC-DC converter

This study provides a step-by-step analysis of closed-loop parametric system identification for DC-DC buck converter. In closed-loop parametric identification, input–output experimental data are used to estimate the transfer function coefficients of DC-DC buck converter. For system identification purpose, a high-frequency perturbation signal is injected in to the closed-loop system which acts as an input signal for identification experiment. Different input–output models such as Auto-Regressive eXogenous, Auto-Regressive Moving Average with eXogenous, output error, and Box–Jenkins are used to model the converter structure and prediction error method is used to estimate the parameters. Model validation schemes are used to validate the estimated model. Simulation and experimental analysis have been provided to validate the results obtained.

Modeling and Optimization of Wear Rate of AISI 2507 Super Duplex Stainless Steel

The present work attempts to study the parameters influencing wear, namely, applied load, heat-treated temperature, sliding velocity, and sliding distance using statistical Design of Experiments (DOE) and Response Surface Methodology (RSM). The wear behavior of super duplex stainless steel was evaluated under dry sliding conditions. A three-level Central Composite Design (CCD) based non-linear model was used to establish input-output relationship based on the collected experimental input-output data. Surface plots were used to study the influence of applied load, heat-treated temperature, sliding distance, and sliding velocity on the wear rate of super duplex stainless steel. The wear rate was observed to vary nearly non-linearly with applied load and linearly with the rest of the input parameters. Analysis of Variance (ANOVA) was conducted to test the statistical adequacy of the non-linear model developed. Applied load and heat-treated temperature were found to have a more positive contribution towards the wear rate than other parameters. Although the sliding velocity had a negligible effect, its interaction with applied load and heat-treated temperature had a significant impact on the wear rate. The regression equation developed was tested for its prediction precision with the help of 20 test cases. Further, attempts were also made to determine the optimum combination of input parameters that minimize the wear rate using the Desirability Function Approach (DFA). The objective of minimizing the wear rate was met with the highest desirability value of 1. Confirmation experiments were conducted for the determined optimal set of input parameters of 20 test cases resulting in an average absolute percent deviation in prediction of 6.34% and 5.58%.

A time-domain system identification numerical procedure for obtaining linear dynamical models of multibody mechanical systems

This paper is focused on the development of a numerical procedure for solving the system identification problem of linear dynamical models that mathematically describe multibody mechanical systems. To this end, an input–output representation of the time evolution of a general mechanical system based on a sequence of matrices referred to as Markov parameters is employed. The set of Markov parameters incorporate the state-space matrices that allow for describing the dynamic behavior of a general mechanical system considering the assumption of structural linearity. The system Markov parameters are defined by means of a discretization process applied to the analytical description of a mechanical system, and therefore, they are difficult to obtain directly from observable measurements. However, a state observer can be introduced in order to define a set of observer Markov parameters that can be readily recovered from input–output experimental data. The observer Markov parameters obtained by using a least-square approach allow for computing in a recursive manner the system Markov parameters as well as another discrete sequence of matrices referred to as observer gain Markov parameters. Subsequently, the system and observer gain Markov parameters identified from observable input–output data are used for constructing a sequence of generalized Hankel matrices from which a state-space model of the mechanical system of interest can be extracted. This fundamental step of the identification procedure is performed in the algorithm elaborated in this work employing a numerical procedure which relies on the use of the Moore–Penrose pseudoinverse matrix obtained by means of the singular value decomposition. In the paper, the principal analytical and numerical aspects of the proposed identification algorithm are described in detail. Furthermore, a numerical example based on a simple vehicle model is discussed in order to verify by means of numerical experiments the effectiveness of the identification procedure developed in this work.