Autoregressive Moving Average With Exogenous Research Articles

THE IMPACT OF ENVIRONMENTAL TAXES ON TRANSPORTATION AND STORAGE ENTERPRISES’ DEVELOPMENT – THE CASE OF BALKAN COUNTRIES

Sustainable development counteracts climate change and strives for a good quality of life. It is a development based on economic, social and environmental goals which should be evaluated. Environmental taxes have to ensure enterprises’ development follows sustainability principles. The basis of environmental taxes is a physical unit of harmful substance emissions with a proven negative impact on the environment. The article’s main objective is to assess the impact of environmental taxes (Envtax) on the sustainable development of the transportation and storage enterprises (Susd) (NACE Rev. 2: H) from 2008 to 2018. We created integrated indicators of sustainable development and its components, single-equation and multi-equation models (the OLS estimation) and Model Autoregressive Moving Average with eXogenous (ARMAX) to verify the research hypothesis. The research results indicate that energy (Entax t), transport (Trtax) and pollution taxes (Poltax) from current and previous period significantly affect the development of transportation and storage enterprises within sustainability principles (economic: Ed, social: Sd, and environmental: Envd). There is a strong variation in the strength and direction of the impact of environmental taxes on the development of the transportation and storage enterprises following sustainability principles in Bulgaria, Croatia, Romania, and Slovenia from 2008 to 2018. The results indicate that taxes are not effectively used and that implementing reforms in the European Union in this area is the right direction.

Optimal tuning of PI controller using system identification for two-phase boost converter for low-voltage applications

<span lang="EN-US">This paper presents modeling and hardware implementations of a two-phase DC-DC boost converter by using the system identification approach. The main objective of this research was to study new methods to obtain the values of the constants for the proportional-integral (PI) controller. Existing methods are time-consuming, since the values of the constants for the PI controller need to be calculated. The system identification approach for the closed-loop boost converter saves more time. This method has the fastest technique to find constants </span><em><span lang="EN-US">K<sub>p</sub></span></em><span lang="EN-US"> and </span><em><span lang="EN-US">K<sub>i</sub></span></em><span lang="EN-US"> for the closed-loop two-phase boost converter. To model a two-phase boost converter using the system identification approach, input duty cycle and output voltage are collected in the time domain data. In this study, the transfer function (TF) model, the autoregressive moving average with exogenous (ARMAX) model and the output-error (OE) model were used to generate a mathematical model. To perform the closed-loop analysis, constants </span><em><span lang="EN-US">K<sub>p</sub></span></em><span lang="EN-US"> and </span><em><span lang="EN-US">K<sub>i</sub></span></em><span lang="EN-US"> were obtained based on the generated mathematical model from the system identification approach. The result from the experiment shows that the percentages of overshoot for the TF, ARMAX and OE models were 19%, 25.36% and 24.6%, respectively. The output voltage ripples obtained for all three models were less than 5% of output voltage.</span>

Parametric system identification of large‐scale structure using decoupled synchronized signals

SummaryLarge‐scale structures are subjected to environmental loads or frequent seismic motion with irremediable effect. These loads have often multidirectional actions on structures, and it couples their responses and leads to multiple‐input multiple‐output (MIMO) problem. The complexity of MIMO model and the relative time‐delays in sensing networks are among major sources of error in dynamic properties identification in large scale structures. This study proposed a parametric‐time domain method to reduce the negative effect of these problems. For this purpose, the contribution of each input in the output signals is determined using QR decomposition and converts a MIMO problem into multiple single‐input multiple‐output (SIMO) ones. In this regard, an Autoregressive Moving Average with eXogenous (ARMAX) model is implemented on decoupled signals for modal identification. Further, for time synchronization of records, a cross‐correlation function has been used to achieve more precise results. The method was employed real strong‐motion response recorded by different sensors at a high rise 64‐story concrete building. Results demonstrate the promising precision of the proposed algorithm for identifying current structural modal properties under real earthquake excitations. Hence, structures can be monitored efficiently along seismic experiences to detect any possible variations in their structural features. The comparison between the output of the proposed method and previous study indicates a considerable improvement on accuracy of the estimated model property particularly on mode shapes.

Simulation and modeling of two-level DC/DC boost converter using ARX, ARMAX, and OE model structures

<span>This paper presents simulation and modeling of two-level DC/DC boost converter using system identification technique. The main objective is to identify the unknown mathematical model from designated converter that has 2 modes of operation. Signals from the converter were processed based on the impulse response from input and output voltages, which were in time domain data. Auto Regressive with eXogenous (ARX), Auto Regressive Moving Average with eXogenous (ARMAX), and Output-Error (OE) model structure techniques had been employed to generate a model from the converter, whose validation was based on coefficient of determination (R<sup>2</sup>) or best fits criterion. The result shows that the ARX model structure produced the best model with 94.03%, compared to ARMAX and OE with 93.70% and 92.25% respectively. In terms of stability for open-loop analysis, the ARX model structure gave the most stable system.</span>

Estimation of Small Unmanned Aerial Vehicle Lateral Dynamic Model with System Identification Approaches

Modeling of unmanned aerial vehicle (UAV) with system identification is very important in terms of its model-based effective control. The modeling of UAV is required for aircraft crashes, analyzing autonomous aircrafts, preventing external disturbances, pre-flight analysis. However, since UAV has nonlinear inherent dynamics including inherent chaoticity and fractality, it becomes difficult to obtain a mathematical model under external disturbance. In this study, some of the inherent nonlinear dynamics of UAV are linearized and the model of UAV is obtained by system identification approaches under external disturbance. The linearized lateral dynamics of a fixed wing UAV is used in this study. Further, the flight motion equations applied to fixed wing UAV have been utilized for obtaining the coefficients of lateral model for straight and level flight. The roll angles are calculated using transfer functions for aileron, rudder and deflections inputs. The autoregressive exogenous (ARX), autoregressive moving average with exogenous (ARMAX) and output error (OE) parametric system identification approaches are performed to estimate UAV lateral dynamic system response as using empirical input-output data sets. The accuracy of parametric model estimation and model degrees are compared for different external disturbance effects.

Retracted: Investigate the forecasting technique for electricity load demand in urban area via statistical approach

Electricity energy as a product cannot be stored as it should be generated as soon as it is demand. Therefore, load demand forecasting plays a vital role in power system planning management in order to meet the load demand requirement particularly during the peak demand. In addition, the importance of electricity load demand forecasting is to support rapidly the economic growth in Malaysia and urban population. The purpose of the current study was to evaluate the performance of Regression model that include Multiple Stepwise Regression, Autoregressive Integrated Moving Average (ARIMA) with Regression and Autoregressive Moving Average with Exogenous (ARIMAX) models in forecasting electricity load demand in Johor Bahru using the load, temperature, population and economic data from 2005 to 2011. The results attained were validated with Root Mean Square Error (RMSE) as a forecasting accuracy criteria.By comparing the regression model, the findings yielded that the Multiple Stepwise Regression method was the most appropriate model for forecasting electricity load demand in Johor Bahru. Load Demand Forecasting, Multiple Stepwise Regression, ARIMA, ARIMAX

Innovative substructure approach to estimating structural parameters of shear structures

An innovative substructure approach is proposed for estimating the structural parameters of shear structures from the acceleration responses of individual substructures. Two parallel methods are created to form single-degree-of-freedom models of each substructure. The behavioral characteristics of these substructure models chiefly depend on the structural parameters of the edges of the component substructure, which is separate from the shear structure. To obtain structural parameters from the substructure accelerations, discrete substructure models with accelerations are generated using Newmark's method and are found similar to the autoregressive moving average with exogenous (ARMAX) inputs models. Sophisticated techniques for solving ARMAX models are used to process the accelerations and to extract the structural parameters of the substructures. A linear relationship among model coefficients of the discrete substructure models and ARMAX models is discovered that provides an accurate and simple way to identify all the substructure parameters. A numerical simulation of a 10-story shear structure during earthquake is performed to verify this substructure approach, where the factors of the size of the substructure and the noise disturbance are considered. Finally, this substructure approach is used to identify a structural model and reproduce the structural responses of a five-story three-dimensional structure in a shaking-table experiment.

Modelling of Baker’s Yeast Production

In the present work, parametric models for the control of bioreactor temperature have been applied. Various order discrete time model parameters were evaluated theoretically and experimentally. Two types of input signals were used as external force to determine Auto Regressive Moving Average with Exogenous (ARMAX) model parameters with Recursive Least Square (RLS) parameter estimation algorithm. The third order ARMAX model is utilized, and compared with the second order one. Ternary and square disturbances are given to the cooling water flow rate which can be chosen as manipulating variable in closed loop cases. System response is monitored continuously and the model parameters are calculated. The models with experimentally identified parameters are compared with ones that their parameters are identified theoretically.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies.

Self-Tuning GMV Control of Glucose Concentration in Fed-Batch Baker’s Yeast Production

A detailed system identification procedure and self-tuning generalized minimum variance (STGMV) control of glucose concentration during the aerobic fed-batch yeast growth were realized. In order to determine the best values of the forgetting factor (λ), initial value of the covariance matrix (α), and order of the Auto-Regressive Moving Average with eXogenous (ARMAX) model (n a, n b), transient response data obtained from the real process wereutilized. Glucose flow rate was adjusted according to the STGMV control algorithm coded in Visual Basic in an online computer connected to the system. Conventional PID algorithm was also implemented for the control of the glucose concentration in aerobic fed-batch yeast cultivation. Controller performances were examined by evaluating the integrals of squared errors (ISEs) at constant and random set point profiles. Also, batch cultivation was performed, and microorganism concentration at the end of the batch run was compared with the fed-batch cultivation case. From the system identification step, the best parameter estimation was accomplished with the values λ = 0.9, α = 1,000 and n a = 3, n b = 2. Theoretical control studies show that the STGMV control system was successful at both constant and random glucose concentration set profiles. In addition, random effects given to the set point, STGMV control algorithm were performed successfully in experimental study.

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.

Identification and non-linear control strategy for industrial pneumatic actuator

In this paper, a combination of nonlinear gain and proportional integral derivative (NPID) controller was proposed to the trajectory tracking of a pneumatic positioning system. The nonlinear gain was employed to this technique in order to avoid overshoot when a relatively large gain is used to produce a fast response. This nonlinear gain can vary automatically either by increasing or decreasing depending on the error generated at each instant. Mathematical model of a pneumatic actuator plant was obtained by using system identification based on input and output of open-loop experimental data. An auto-regressive moving average with exogenous (ARMAX) model was used as a model structure of the system. The results of simulation and experimental tests conducted for pneumatic system with different kind of input namely step, sinusoidal, trapezoidal and random waveforms were applied to evaluate the performance of the proposed technique. The results reveal that the proposed controller is better than conventional PID controller in terms of robust performance as well as show an improvement in its accuracy.

Robust Self-Tuning Regulator of Time-Varying Linear Systems with Bounded External Disturbances

robust self-tuning regulator of a class of linear systems, which can be described by the input-output Auto-Regressive Moving Average with exogenous (ARMAX) mathematical model with unknown and time-varying parameters, at bounded external disturbances is developed. A scheme of polynomial approximation has been applied to approximate the unknown and time-varying parameters of systems. The modified recursive extended least squares RELS estimation algorithm with a relative dead zone is proposed and applied to estimate the unknown and time-varying parameters intervening in the ARMAX mathematical model. The formulation of the explicit schemes of self-tuning regulation problem is resolved by using the minimum variance output or the generalized minimum variance output. The obtained control law, which is an optimal solution of minimizing a correspondent criterion, permit to reduce the effect of noise upon the output of system. An example of numerical simulation illustrates the effectiveness of the explicit schemes of self-tuning regulator and presents the performances by using the modified recursive extended least squares estimation algorithm with a relative dead zone in a step of the parametric estimation of a linear time-varying systems.

A Two-stage ARMAX Identification Approach Based on Bias-eliminated Least Squares and Parameter Relationship between MA Process and Its Inverse

In this paper, a two-stage approach is proposed for the parameter identification of autoregressive moving average with exogenous (ARMAX) variable model. The proposed approach identifies the autoregressive part with exogenous variable (ARX) by a bias-eliminated least squares method, and the moving average (MA) part by utilizing the parameter relationship between MA process and its inverse. Finally, the noise variance can be computed by using the identified MA parameters. Numerical simulations validate the effectiveness of the proposed approach.

Experimental investigation of TARMAX model for modeling of hydrodynamic forces on cylinder-like structures

A new approach that models lift and drag hydrodynamic force signals operating over cylindrical structures was developed and validated. This approach is based on stochastic auto regressive moving average with exogenous (ARMAX) input and its time-varying form, TARMAX. Model structure selection and parameter estimation were discussed while considering the validation stage. In this paper, the cylindrical structure was considered as a dynamic system with an incoming water wave and resulting forces as the input and outputs, respectively. The experimental data, used in this study, were collected from a full-scale rough vertical cylinder at the Delft Hydraulics Laboratory. The practicality of the proposed method and also its efficiency in structural modeling were demonstrated through applying two hydrodynamic force components. For this purpose, an ARMAX model is first used to capture the dynamics of the process, relating in-line forces provided by water waves; secondly, the TARMAX model was applied to modeling and analysis of the lift forces on the cylinder. The evaluation of the lift force by the TARMAX model shows the model is successful in modeling the force from the surface elevation.

Self-tuning control of dissolved oxygen concentration in a batch bioreactor

Two different set profiles were used during the dissolved oxygen (DO) level control in a bioreactor in which Saccharomyces cerevisiae NRRL Y-567 was produced under aerobic condition. The performance of self-tuning generalised minimum variance (STGMV) algorithm was observed and compared to conventional PID control performance. Airflow rate and agitation speed were examined as manipulated variables to determine the maximum local liquid phase volumetric mass transfer coefficient ( K L a ). Maximum K L a value was determined at 600 rpm agitation speed and 3.4 L/min airflow rate. An auto regressive moving average with eXogenous (ARMAX) model was used as the process model in the control algorithm and the system identification step. Model and tuning parameters were estimated on-line using recursive least square (RLS) method. Experimental results show that self-tuning DO concentration control by using the set profile which has five step changes performed 90% better than the performance of the same control strategy which utilized the constant set value of DO concentration. In conventional PID control applications, using the set profile which has five step changes brought about 92% decrease in the performance criteria when it is compared with the case using the constant set value of DO concentration.

Adaptive Control of S.cerevisiae Production

The growth medium temperature of a bioreactor, in which S.cerevisiae was produced at aerobic condition, was controlled applying adaptive PID technique. Glucose was used as an energy and carbon source in the growth medium, and an exothermic reaction took place as a result of the glucose consumption of the microorganism under aerobic condition. Then the specific growth rate of the microorganism was inhibited by the increasing growth medium temperature. To maintain the growth medium temperature at its set point which ensures maximumyeast productivity and quality in the bioreactor, non-linear adaptive PID and linear adaptive PID control systems were employed numerically. The bioreactor in which S.cerevisiae yeast was produced under the aerobic condition was considered as a batch system with respect to mass balance. The heat balance between the cooling jacket and the growth medium in the bioreactor was considered as a continuous system with respect to energy. These approximationswere taken as a basis to develop the simulation work. In the simulation studies, models for the bioprocess were developed by using mass and energy balance equations and solved by utilizing the Runge–Kutta–Felhberg method. The control algorithm developed was added to the simulated model. For the adaptive PID control system, Non-linear Auto Regressive Moving Average with eXogenous (NARMAX) and Auto Regressive Moving Average with eXogenous (ARMAX) models were used, and relevant algorithms were developed on the basis of these black box models. The coefficients of the polynomials for both type of models were estimated from the relevant identification method. It was observed that non-linear adaptive PID control results were better than the results of linear adaptive PID and conventional PID control systems.