External Input Model Research Articles

Identification of a different design of a photovoltaic thermal collector based on fuzzy logic control and the ARMAX model

This study investigates the effects of absorber design on the performance of a water-based photovoltaic thermal collector (PVT). In this regard, three different absorber designs with the same pipe diameter and length were tested and compared experimentally in Cairo, Egypt, during summer 2021. Furthermore, artificial intelligence techniques were used to create a model to identify the PVT system and define the input–output relationship in state space representation. This is to get a more thorough understanding and deeper evaluation of the PVT performance with various absorber designs to achieve the best possible system performance under varied operating situations and weather. The fuzzy logic control is examined by Autoregression Moving Average using an external input model (ARMAX), which relies on the autoregression moving average to achieve system performance. According to the study's findings, at a maximum water flow rate of 3 L/min, the proposed PVT system can convert solar energy with an overall efficiency of 69.6 %, 64.6 %, and 63.5 %, respectively, with a pressure loss of about 29.5 kPa, 29.33 kPa, and 24.79 kPa for absorbers A, B, and C. Furthermore, the challenging suggested fuzzy control system for the PVT system provides knowledge to comprehend the system complexity since the connection is formed by connecting 5 inputs with 3 outputs through its representation in the state space representation.

Bio-Tribo-Acoustic Emissions: Condition Monitoring of a Simulated Joint Articulation

Acoustic emissions have been used to interpret the frictional processes observed in a simulated metal-on-polymer joint replacement articulation during in vitro testing. The coefficient of friction profile is predicted from AE features using a nonlinear autoregressive neural network with an external input model, and the evolution of surface damage is identified using k-means clustering of the distribution of emission types from running-in to prolonged sliding states. The predicted coefficient of friction profiles were found to exhibit a similar response to the actual coefficient of friction profiles. Clustering showed that a higher percentage of continuous emissions are generated during the prolonged sliding stage, indicating sliding friction being the most dominant process during that state. The findings of this study provide a significant pathway toward achieving the potential of AE testing as a more intuitive and dynamic process of monitoring the tribological conditions of artificial joints and diagnosing the pathologies of the natural joints.

Temperature prediction of combustion level of ultra-supercritical unit through data mining and modelling

This paper presents the dynamic prediction model of different combustion levels temperature in the furnace based on the data mining and modelling. The temperature of the combustion level is the important parameter to characterize the combustion state. The infrared temperature measuring device is developed to measure the temperature of different combustion levels. Based on the operation data of 660 MW ultra-supercritical coal-fired unit, the dynamic prediction models of different combustion levels (bottom, middle and upper) temperature under different load conditions are proposed by using the autoregression integrated moving average with external input model and autoregressive distributed lag model. For the different load conditions, including high-load, middle-load and low-load, variable importance in the projection algorithm is used to obtain the key variables that affect the combustion temperature of different levels. The different prediction models are compared and analyzed. The feasibility and effectiveness of the developed dynamic model for predicting the combustion level temperature using industrial data is validated. Results demonstrate that the proposed approach provides an effective tool for predicting temperature of combustion level and the potential of further improving combustion optimization.

Optimized Inverse Nonlinear Function-Based Wiener Model Predictive Control for Nonlinear Systems

In model predictive control applications, the static nonlinear function of the Wiener model is an obstacle to forming a quadratic cost function. That case results in suboptimal control actions. As is known, a Wiener model predictive controller makes predictions employing the linear block of the Wiener model, and so its control solution is optimal due to the quadratic cost function. If the actual process output is used as a parameter in the empirical model, the inverse of the nonlinear function cannot be obtained as the roots of the nonlinear function in an offline manner, and the Wiener model predictive controller cannot be developed. Having regard to those situations, this paper introduces an offset-free Wiener model-based predictive controller with optimized inverse nonlinear function for the perturbed neutralization processes. Here, to facilitate accounts in the prediction horizon, an autoregressive with an external input model is preferred as the linear element of the Wiener model, and the least-squares-based optimization method is proposed to get the inverse of the nonlinear function. In this way, the model correction term can be employed to achieve an offset-free controller just as employed in the linear model predictive controller. Finally, the control performance of the developed controller is confirmed via comparative MATLAB/Simulink studies.

Time-Varying Nonlinear Causality Detection Using Regularized Orthogonal Least Squares and Multi-Wavelets With Applications to EEG

A new transient Granger causality detection method is proposed based on a time-varying parametric modeling framework, and is applied to the real EEG signals to reveal the causal information flow during motor imagery (MI) tasks. The time-varying parametric modeling approach employs a nonlinear autoregressive with external input model, whose parameters are approximated by a set of multi-wavelet basis functions. A regularized orthogonal least squares (ROLS) algorithm is then used to produce a parsimonious or sparse regression model and estimate the associated model parameters. The time-varying Granger causality between nonstationary signals can be detected accurately by making use of both the good approximation properties of multi-wavelets and the good generalization performance of the ROLS in the presence of high-level noise. Two simulation examples are presented to demonstrate the effectiveness of the proposed method for both linear and nonlinear causal detection respectively. The proposed method is then applied to real EEG signals of MI tasks. It follows that transient causal information flow over the time course between various sensorimotor related channels can be successfully revealed during the whole reaction processes. Experimental results from these case studies confirm the applicability of the proposed scheme and show its utility for the understanding of the associated neural mechanism and the potential significance for developing MI tasks based brain-computer interface systems.

Input–Output Stabilization of Linear Systems on<tex>$BBZ$</tex>

A formal framework is set up for the discussion of generalized autoregressive with external input models of the form Ay=Bu, where A and B are linear operators, with the main emphasis being on signal spaces consisting of bounded sequences parametrized by the integers. Different notions of stability are explored, and topological notions such as the idea of a closed system are linked with questions of stabilizability in this very general context. Various problems inherent in using /spl Zopf/ as the time axis are analyzed in this operatorial framework.

WaveARX Neural Network Development for System Identification Using a Systematic Design Synthesis

The WaveARX network, a new neural network architecture, is introduced. Its development was motivated by the opportunity to capitalize on recent research results that allow some shortcomings of the traditional artificial neural net (ANN) to be addressed. ANN has been shown to be a valuable tool for system identification but suffers from slow convergence and long training time due to the globalized activation function. The structure of ANN is derived from trial and error procedures, and the trained network parameters often are strongly dependent on the random selection of the initial values. There are not even guidelines on the number of neurons needed. Also, few identification techniques are available for distinguishing linear from nonlinear contributions to a system's behavior. The WaveARX integrates the multiresolution analysis concepts of the wavelet transform and the traditional AutoRegressive eXternal input model (ARX) into a three-layer feedforward network. Additional network design problems are solved as the WaveARX formalisms provide a systematic design synthesis for the network architecture, training procedure, and excellent initial values of the network parameters. The new structure also isolates and quantifies the linear and nonlinear components of the training data sets. The wavelet function is extended to multidimensional input space using the concept of a norm. The capabilities of the network are demonstrated through several examples in comparison with some widely used linear and nonlinear identification techniques. Separately, the wavelet network of the WaveARX model is shown for the example investigated to have a better performance than two other existing wavelet-based neural networks.

Synchronous Machine Parameter Identification by a Multitime Scale Technique

The authors propose a novel method for synchronous machine parameter identification. They use the multi-time-scale decompositions to reduce the operational reactances. The identification method is based on the frequency responses. The synchronous machine is at standstill and supplied by pseudorandom binary sequence voltages. The output data are the currents in the windings. All the Park's model parameters of an alternator of 13.5 kVA/19.5 A/50 Hz are estimated by an autoregressive external input model structure. >

Model identification in extrusion cooking

In this paper extrusion cooking is reduced to a single input-single output process and identification techniques are applied to derive transfer function models. A study is reported of the open-loop relationships between screw speed as the manipulated variable and die pressure as the controlled variable when extruding wheat flour with a twin-screw co-rotating extruder. Step tests were performed in various ranges from different working points. The auto-regressive moving average and external input model structure was used; in this model the parameters are estimated by the output error with extensive predictive model. The pressure responses to these step tests are complicated in that the steady-state gain is not constant, the identified transfer functions are of two types and the estimated model parameters fluctuate.