Autoregressive Moving Average Exogenous Research Articles

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.

Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network

In recent years, slow feature analysis (SFA) has been successfully employed to deal with the air handling unit (AHU) system’s time-varying dynamic properties. However, since the derived slow features are the linear combinations of all original variables, the conventional SFA based methods may suffer from poor model interpretability and result in inaccurate fault detection and diagnosis (FDD) performance. To enhance the dynamic AHU system FDD effectiveness, this paper presents a novel feature sparse representation based SFA algorithm through imposing the sparsity on the slow features, which is called the sparse three-way data based dynamic SFA (STBDSFA). We make two contributions to propose the STBDSFA approach. One contribution is to infuse the feature sparse representation technique into the constructed SFA based monitoring model by performing the sparse restriction on the loading vector, which can effectively eliminate the meaningless variables’ coupling and select the key variable responsible for the fault detection. Before building the STBDSFA algorithm, the other contribution is to construct a new three-way data based dynamic SFA (TBDSFA) monitoring model to handle the AHU system’s two-directional dynamic properties. In the established TBDSFA model, the auto-regressive moving average exogenous (ARMAE) model is first adopted to reveal the variables’ auto-correlation relationships. Then, the multiway data analysis is applied to figure out the batch-wise dynamic property in multiple batch runs. After that, the dynamic SFA model is further set up to sufficiently tackle the time-wise dynamic nature within a batch run. To effectively diagnose the pattern of detected fault, another innovative work is to develop a long short-term memory (LSTM) based fault identification approach to classify the sparse slow features of fault snapshot dataset, owning to the LSTM’s superiority of coping with the dynamic time-varying nature of the sparse slow features. By comparing with some traditional and closely related FDD methods, the case study on the ASHRAE Research Project RP-1312 experimental datasets are performed to verify the performance and effectiveness of the proposed FDD scheme for the AHU system. To be specific, in comparison with the sparse principal component analysis, sparse dynamic SFA, kernel locality preserving projection and sparse three-way data based SFA approaches, the developed STBDSFA based monitoring method not only reveals no fault detection delays to alert the eleven test faults, but also achieves the highest average fault detection rates, i.e., 99.31% for the T2 statistic and 99.77% for the SPE statistic. Contrasted with the support vector machine, temporal convolutional network, convolutional neural network and deep belief network based classification algorithms, the suggested STLSTM based fault identification approach obtains the highest average fault diagnosis rate, i.e., 95.51% for the eleven fault snapshot datasets.

Clustered R3LS: A novel approach for online estimation of power system dominant dynamics

Estimating electromechanical oscillations of power systems plays a crucial role to infer about their stability. In this paper it is proposed a new method in which dominant dynamics of power systems are inferred by analyzing pole estimates produced by several regularized robust recursive least squares (R3LS) implementations operating individually — each one with a different set of autoregressive moving average exogenous (ARMAX) model orders. The proposed method is named “Clustered R3LS”, since it properly adapts the so-called K-means clustering algorithm to automatically model regions of pole agglomeration produced by these individual R3LS implementations. An additional strategy for ignoring (discarding) poles identified as spurious is also part of the proposed Clustered R3LS algorithm. From a practical point of view, Clustered R3LS is here shown to more effectively track dominant dynamics of power systems when compared to Classical R3LS, which is based on analyzing only pole estimates produced by a single R3LS implementation operating with a fixed set of ARMAX model orders that is usually hard to tune. Three case studies are here used to validate the proposed Clustered R3LS method for both ambient and ringdown data. The first and second case studies are based on two well known benchmark models proposed for the analysis of oscillatory dynamics in power systems, whereas the third case study considers actual power system data extracted during a large-scale event from the Brazilian Interconnected Power (BIP) system.

Identification of automatic guided vehicle (agv) based on magnetic guided sensor for industrial material transfer

Automatic Guided Vehicle (AGV) is a material transfer tool that is now widely used in the industry. One of the advantages is that it can transfer material automatically without the need for an operator who has to drive it. AGV using a magnetic guided sensor is one of the most widely used types. So that the tool can run according to the planned trajectory, it is necessary to design a controller that matches its characteristics. The design of the controller is needed identification of the model of the plant to know the characteristics of the plant. Identification with the Extended Least Square method is a form of parameter estimation recursively through a quadratic error gradient formulation with a criterion function involving all errors. The structural approach model used linearly uses Auto-Regressive eXogenous (ARX) and Auto-Regressive Moving Average eXogenous (ARMAX). The validation process of the model uses the Means Square Error (MSE) criteria and the FIT percentage. Based on the results obtained by the best plant with ARMAX model structure with MSE values 82.14 and percentage of FIT value 89.98%.

Minimum-variance self-tuning regulator in EDM drilling processes for ultra-high-aspect-ratio small holes

High speed small hole electrical discharge machining (EDM) drilling processes can easily become unstable, due to changing flushing conditions, inappropriate preset electrical parameters, and machining debris residues. Although there are already successful attempts in controlling EDM processes, cost-effective adaptive control of high speed small hole EDM drilling has not been reported. Process stability can contribute to the improvement of machining efficiency and surface finish. To maintain a higher process stability, a minimum-variance self-tuning regulator (MVSTR) is designed based on an auto-regressive moving average exogenous (ARMAX) model. To determine specifications of the system, the time and frequency responses are analyzed, and the validation is executed for the offline system model. A series of high aspect ratio holes drilling experiments with different electrode diameters of 0.3 mm, 1.0 mm, and 3.0 mm show that, with the use of the MVSTR control scheme, a higher machining efficiency and a lower tool wear can be achieved as compared with the original controller. Microscopic pictures of drilled holes and electrodes show better surface quality.

Modelling, identification, implementation and MATLAB simulations of multi-input multi-output proportional integral-plus control strategies for a centrifugal chiller system

The objective of this paper is to investigate a new design and real-time implementation approach of a predictive proportional integral-plus (PIP) closed-loop control strategy for a centrifugal chiller HVAC system. By using the analytical model of the plant, linear discrete-time multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) polynomial models were developed. The structure of developed ARMAX models is straightforward, and they are suitable to capture the complex dynamics of the centrifugal chiller plant. Fundamentally, these identified models use the least-squares estimation (LSE) method to evaluate the polynomials coefficients and model parameters implemented using specific tools provided by MATLAB's system identification toolbox. The new modelling approach is beneficial for simulation purposes to prove the efficiency of the proposed closed-loop control strategy, the tracking performance, and its robustness to possible changes in the load disturbance and noise level of measurement sensors.

Application of an Additive Self-tuning Controller for Static Synchronous Series Compensator for Damping of Sub-synchronous Resonance Oscillations

In this paper, an additive self-tuning (ST) control scheme is presented for a static synchronous series compensator (SSSC) to improve performance of conventional PI control system for damping sub-synchronous resonance (SSR) oscillations. The active and reactve series compensation are provided by a three-level 24-pulse SSSC and fixed capacitor. The proposed ST controller consists of a pole shift (PS) controller and a recursive least squares (RLS). The RLS identifier algorithm is used to estimate parameters of auto regressive moving average exogenous (ARMAX) model. With this scheme, there is no any necessity for retuning the PI controller parameters under different operational circumstances of the system. The SSR analysis are performed using eigenvalue analysis, transient simulation and FFT analysis. The considered test system is adapted from IEEE First Benchmark Model. As shown in the results, the proposed additive ST controller has an effective and acceptable performance for damping of SSR under different disturbances and operating conditions. It should be noted here that all digital simulations have been done by using MATLAB.

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. This paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. The results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.

MPC control for improving energy efficiency of a building air handler for multi-zone VAVs

The performance and energy saving of building heating, ventilation, and air conditioning (HVAC) systems can be significantly improved by the implementation of intelligent and optimal controls. This article presents a parametric modeling approach and a system-level control design to improve the energy efficiency of building HVAC systems. We present an auto-regressive moving average exogenous (ARMAX) model that relates the return air temperature and flow rate of an air-handling-unit (AHU) for multi-zone variable air volumes (VAVs). We also develop a model predictive control (MPC) to minimize the energy consumption of the AHU. The control tracks the set points subject to thermal load constraints from lower level VAVs. The optimal control can achieve over 27.8% energy saving on average as compared to the baseline control that is originally installed in the building, and can closely track the supply air flow rate and setpoint of room temperature. In this paper, all the data processing, model validation and implementation of the control algorithm are based on extensive measurements collected from an office building on the campus of the University of California, Merced.

A quality-related fault detection approach based on dynamic least squares for process monitoring

The issue of quality-related fault detection has attracted much attention in recent years. Partial least squares (PLS) is considered as an efficient tool for predicting and monitoring. However, due to the fact that PLS performs an oblique projection to input space, it is not suitable for quality-related fault detection. On the other hand, PLS is a static method which cannot be used in dynamic systems. In this paper, a dynamic least squares approach is developed by using the structure of auto-regressive moving average exogenous (ARMAX) time-series model. Furthermore, augmented input matrix is decomposed into two orthogonal parts according to their correlations with output, such that quality-related fault detection can be utilized by designing appropriate statistics in two subspaces corresponding to the two parts. The proposed approached is simple and effective for systems with dynamic input and static output, which is a common case for most industrial processes. A numerical example and an industrial process simulator are applied to test the performance of the proposed approach.

PERAMALAN KUNJUNGAN WISATAWAN MENGGUNAKAN MODEL ARMAX DENGAN NILAI KURS DAN EKSPOR-IMPOR SEBAGAI FAKTOR EKSOGEN

Forecasting is science to estimate occurrence of the future. This matter can be conducted by entangling intake of past data and place to the next period with a mathematical form. This research aims to estimate the number of foreign tourists visiting Bali models using autoregressive moving average exogenous (ARMAX). The data used in this study is the number of tourists in Australia and the number of tourists in the RRC as a variable Y, and foreign currency exchange rate AUD, Chinese Yuan, and Export Import as the X factor from the period July 2009 to July 2014. In the analysis can be obtained in the best ARMAX models of the number of tourists in Australia is ARMAX(1,2,2) and the best model of the number of tourists in the RRC does not exist because the data for the ARMAX model parameters tourists no significant RRC.

게인 스케줄링을 이용한 광대역 온도제어기의 설계

This paper focused on the design of an efficient temperature controller for a plant with a wide range of operating temperatures. The greater the temperature difference a plant has, the larger the nonlinearity it is exposed to in terms of heat transfer. For this reason, we divided the temperature range into five sections, and each was modeled using ARMAX(auto regressive moving average exogenous). The movement of the dominant poles of the sliced system was analyzed and, based on the variation in the system parameters with temperature, optimal control parameters were obtained through simulation and experiments. From the configurations for each section of the temperature range, a temperature-based gain-scheduled controller (TBGSC) was designed for parameter variation of the plant. Experiments showed that the TBGSC resulted in improved performance compared with an existing proportional integral derivative (PID) controller.

Estimating electromechanical modes and mode shapes using the multichannel ARMAX model

A method of estimating the electromechanical modes and mode shapes from multiple synchrophasors is presented in this paper. The approach is based upon identifying the transfer function representation of the state space model of the linearized power system through the estimation of a multichannel AutoRegressive Moving Average eXogenous (ARMAX) model. The relationship between the transfer function and the ARMAX model, which is fully derived here, is shown to be general enough that virtually any multichannel ARMAX estimation algorithm may be applied to this approach. The two-stage least squares (2SLS) algorithm is used here for illustrative purposes. The proposed method is validated with the Monte Carlo procedure using simulated data from a reduced-order model of the Western Electricity Coordinating Council (WECC) system. The performance of the new method is compared with existing multichannel mode and mode shape estimation techniques and is seen to provide increased estimation accuracy with a decrease in computation time. The proposed method is further validated with measured data from the WECC system.

FEM Calibrated ARMAX Model Updating Method for Time Domain Damage Identification

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

Weak fault monitoring method for batch process based on multi-model SDKPCA

In industrial manufacturing, most batch processes have the dynamic and nonlinear features in nature. To ensure both quality consistency of the manufactured products and safe operation of this kind of batch process, a number of multivariate statistical analyses, including multiway principal component analysis (MPCA), batch dynamic kernel principal component analysis (BDKPCA), have been developed in recent years. However, these methods can't effectively detect the weak faults due to large fluctuations in the initial conditions, because the weak faults are submerged to the fluctuations in the poor initial conditions. In order to improve the performance of the weak fault detection, a new nonlinear dynamic batch process monitoring method, called multi-model single dynamic kernel principal component analysis (M-SDKPCA), is proposed in this paper. The multi-model methodology is based on BDKPCA. The method firstly integrates kernel PCA (KPCA) and auto-regressive moving average exogenous (ARMAX) time series model for each batch data at each stage to build SDKPCA. Then hierarchical clusters are obtained through load matrix similarity among SDKPCA models. At different stages, multiple model structures are constructed along with the variation of the cluster number. The monitoring method proposed in this paper was applied to fault detection for benchmark of fed-batch penicillin production. In both off-line analysis and on-line batch monitoring, the proposed approach shows better performance than MKPCA and BDKPCA.

Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data.

Convergence of Recursive Identification for ARMAX Process with Increasing Variances

The autoregressive moving average exogenous (ARMAX) model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average (MA) part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results.

Vibration control of an arc type shell using active constrained layer damping

Active constrained layer damping (ACLD) combines the simplicity and reliabilityof passive damping with the low weight and high efficiency of active control toattain high damping characteristics. A cantilevered shell type arc that is made ofcarbon/epoxy composite material is chosen to investigate the potential of ACLDeffectiveness. Using a system identification method, the arc/ACLD system ismodelled as an ARMAX (autoregressive moving average exogenous) model andconverted into a state space form to design optimal control laws. A linear quadraticregulator (LQR) controller is used to find optimal control gains. The performance ofthe ACLD system is determined and compared with passive constrained layerdamping (PCLD) in order to demonstrate the effectiveness of the ACLD treatment.

Active control of a flexible smart beam using a system identification technique based onARMAX

A study on active vibration suppression for a flexible structural beam via a systemidentification approach was experimentally performed. The beam, incorporating a pair ofpiezoceramic transducers as actuators and a set of surface-bonded strain gauges as sensors,was evaluated. The relationship between the input control voltage applied on theactuators and the consequently induced strain, measured by the sensor, was thenderived. An active control system, considering both the actuators and sensor, wasconfigured using the ARMAX (auto-regressive moving average exogenous) model. Acontinuous signal with step waveform was selected as the input excitation. Adigital-signal-processor-based real-time adaptive vibration control algorithm, supported byAgilent® E1415A, SCPs 1511 and SCPs 1532, was developed, and an algorithm was establishedusing a pole placement control method, so as to achieve the desired closed-loop control.The effectiveness of the ARMAX model was examined by comparing it with the ARX(auto-regression with extra inputs) model. The results show good performance of theARMAX model for system identification purposes and excellent effectiveness for activestructural vibration control.

Application of adaptive model-predictive control to a batch MMA polymerization reactor

An adaptive model-predictive control (AMPC) algorithm was applied to the temperature control of a batch polymerization reactor for polymethylmethacrylate (PMMA) and the tracking performance for set point changes was investigated. An experimental control system for the polymerization reactor was constructed with cascade structure. The process parameters of auto-regressive moving average exogenous (ARMAX) model were estimated by the recursive least squares (RLS) method with self-varying forgetting factor. To make the estimation robust, we introduced a dead-zone and normalization algorithm in the RLS estimator. The model-predictive controller of unified approach was then implemented to control the reactor temperature on the basis of this process model. This indirect adaptive model-predictive controller showed better performance than the conventional PID controller for tracking set point changes, especially in the latter part of the reaction course when the gel effect became significant. Also, the control experiments were successfully conducted for tracking the optimal temperature trajectory which would yield polymer product having desired properties.