Measured Input Data Research Articles

Learning-based robust output tracking control for unknown discrete-time nonlinear systems with dynamic uncertainty

Although numerous approximate dynamic programming (ADP) methods have been proposed and applied to robust control problems, research on the use of ADP methods for robust tracking control in nonlinear systems remains limited, especially for discrete time (DT) nonlinear systems. In this paper, we propose an efficient robust learning-based output tracking control method, referred to as r-LTC, for a class of unknown DT nonlinear systems with dynamic uncertainty. The objectives are achieved by: 1) using a system identification technique based on network-type coefficients Auto-Regressive with eXogenous variables (ARX) model to identify completely unknown systems; 2) reformulating the identified model into a state space model in terms of deviations from the reference trajectory, such that the robust tracking problem can be converted into a robust regulation problem; 3) transforming the robust regulation problem into an equivalent optimal regulation problem for a nominal system with a modified utility function; 4) alternately implementing the performance evaluation and control policy update through two neural networks (NNs) to numerically solve DT Hamilton-Jacobi-Bellman (HJB) equation, such that the approximated optimal feedback control inputs for nominal system can be obtained, allowing for realization of all unknown bounded uncertainties. The proposed method does not require a state observer and any steady state knowledge, only the measurable system input and output data are utilized. The convergence of NNs’ approximate weights and stability of the closed-loop uncertain systems are rigorously proven using the Lyapunov method. Lastly, a case study is performed using real data collected from the Shenzhen Metro Line 12 tunneling project to examine the performance of the proposed r-LTC algorithm in terms of the set value tracking accuracy, control system stability, and robustness against different types of disturbances.

Cascade MFAC-based data-driven control for aircraft in 4D trajectory tracking

This study aims to explore a trajectory tracking control approach in four-dimensional (4D) trajectory operation by employing cascade model-free adaptive control (MFAC) technique. The primary objective is to tackle the complexities associated with modeling aircraft mechanisms in the context of tracking control. By utilizing measurable input and output data, an equivalent dynamic linearized data model of the aircraft is constructed. Subsequently, position and speed controllers are developed based on MIMO-CFDL-MFAC and SISO-CFDL-MFAC, respectively. These controllers are then integrated to form an active-follower control system. Finally, a numerical simulation was conducted to track the entire 4D trajectory, encompassing climb, cruise, and descent stages, using historical data from flight CX8176 as a reference trajectory. Unlike previous researches, the proposed method eliminates the requirement for an aircraft mechanism model in the controller design, and it is verified using real flight data as a reference trajectory. The findings demonstrate that the cascade MFAC method can effectively minimize trajectory tracking errors, while also offering advantages in terms of energy conservation and emission reduction.

A Comprehensive Framework for Data-Driven Building End-Use Assessment Utilizing Monitored Operational Parameters

The availability of building operational data such as energy use and indoor temperature provides opportunities to enhance the traditional building energy performance calculation. Disaggregated building energy use facilitates informed decision-making to identify cost-saving measures efficiently at the individual building and building stock levels. The existing energy performance analysis techniques with measured input data in the literature are fragmented. Moreover, they frequently approach this issue with varying degrees of complexity depending on the available input data, expertise, and time. The procedure of choosing an appropriate method is often cumbersome with limited indication of the usefulness of the outcomes. This study proposes a data-driven framework for end-use load disaggregation through techniques that exploit various kinds of building consumption data. The results demonstrate the use of different techniques for varied applications. Calibrated theoretical calculation, data-driven heat loss coefficient (HLC), and energy signature curve (ESC) are among the proposed methods in the framework that facilitate individual, and urban scale energy decomposition. It is observed that different methods yield unalike outcomes, while their performance is predictable. While the HLC methods are flexible but also highly sensitive to the input parameters, the ESC needs high-frequency time series but provides stable energy decomposition. The ESC is efficient for large-scale analysis and the HLC method for detailed case-specific applications. Calibrated theoretical energy decomposition has a simple workflow and can supplement the current energy performance assessment method, although it entails sufficient input data.

Physics-informed neural network-based surrogate model for a virtual thermal sensor with real-time simulation

In this study, a physics-informed neural network (PINN)-based surrogate model was proposed for a virtual thermal sensor (VTS) with real-time simulation. This surrogate model uses temperature data measured by a small number of physical temperature sensors to estimate the temperature field or the input heat flux in a non-sensored area. In particular, for real-time simulation, the measurement data are set as a direct input feature of the neural network with some modification of the partial differential equation (PDE) loss, and the output temperature over the non-sensored area is immediately predicted accordingly. To validate the performance of the proposed method, we solved three one- and two-dimensional problems. From the results, the proposed method could immediately predict the temperature field and the input heat flux with high accuracy. Moreover, it was found that the predicted temperature field and the input heat flux followed the trend of the ground truth well, even when noise was included in the measurement data. In particular, when an unknown heat flux is imposed, the temperature field and unknown heat flux can be predicted using measurement input data by training with multiple cases for the input heat flux.

Using Rosetta Program in Determining Hydraulic Properties of Three Different Mountainous Textured Soils under Semiarid Conditions of Duhok Iraqi – Kurdistan

This study was carried out in three mountainous different soil textures and two depths. Field measurements of soil texture and bulk density, and laboratory measurements of soil water retention at field capacity (–33 kPa) and permanent wilting point (–1500 kPa), were taken to implement Rosetta. Calculated values of van Genuchten parameters were then compared to measured values. Results showed that Rosetta could be used to obtain values of van Genuchten parameters for a field with different soil textures. The determination coefficient (R2) of It showsthat 50% of determination coefficients(R2) gave more than 0.99 and 30% gave more than 0.98 and the others gave between (0.91-0.97) at applying the (PTFs) at two depths for the studied soils. The Root Mean Square Difference (RMSD) indicated that the lowest values of RMSE were exhibit by all soils (Zawita, Swaratoka and Koradare) and two depths D1 and D2 except D1 of Swaratoka Clay loam soil and at different textures.NSE elevation indicator revealed that the maximum values more than (0.99) were found in Zawita loamy soil and at depth two, D2 whereas the minimum values were noticed in Koradare Silty Loam and at depth two,D2. We conclude that Rosetta is a tool that can be used to calculate van Genuchten parameters the absence of measured values, for the studied soils(Zawita, Swaratoka and Koradare) Rosetta Levels yielded the best results as following: Level 5 gave highest (R2) at applying in (Zawita,D1-Swaratuka and KoradareD2), lowest RMSE and maximum in (Koradare,D2) using the measured input data and thus calculated values ofvan Genuchten parameters can be used as input in simulation models

Development of Photovoltaic Power Generation Prediction Model using ANFIS

Prediction of photovoltaic (PV) performance is important for energy management practices. The power produced from renewable energy sources is uncertain in nature as it is subjected to continuous changing weather conditions. Hence accurate prediction of output power from these sources is difficult task. In this paper Adaptive Neuro-Fuzzy Inference System (ANFIS) based forecast model for predicting the PV power generation is developed. The proposed model is based on back propagation hybrid learning algorithm of ANFIS with four inputs and one output. Experimentally measured input data of 20 KW. PV system installed at Nashik, Maharashtra, India is used for developing prediction model. The inputs are solar radiation (Rad), ambient temperature (Temp), relative humidity (Hum) and day of year for measurement. Photovoltaic power generation is the output of the model. This data is utilized in the training and testing of the proposed model. Results obtained confirm the ability of the developed ANFIS model for assessing the power produced with reasonable accuracy. A comparative study has done between regression analysis and ANFIS. This shows that the ANFIS-model performs much better than regression. The advantage of the ANFIS model is that they do not need more parameters or complicate calculations unlike implicit models. The developed model could be used to forecast the profile of the produced power in uncertain whether conditions. The error due to ANFIS prediction model for energy produced from the given system considered in this research is 6.14 % which is much better when compared with regression analysis whose error is 16 %. The results indicate that this model can potentially be used to estimate and predict PV solar output power.

Stable Numerical Identification of Sources in Non-Homogeneous Media

In this work, we present a numerical algorithm to solve the inverse problem of volumetric sources from measurements on the boundary of a non-homogeneous conductive medium, which is made of conductive layers with constant conductivity in each layer. This inverse problem is ill-posed since there is more than one source that can generate the same measurement. Furthermore, the ill-posedness is due to the fact that small variations (or errors) in the measurement (input data) can produce substantial variations in the identified source location. We propose two steps to solve this inverse problem in some classes of sources: we first recover the harmonic part of the volumetric source, and, in a second step, we compute the non-harmonic part of the source. For the reconstruction of the harmonic part of the source, we follow a variational approach based on the reformulation of the inverse problem as a distributed control problem, for which the cost function incorporates a penalized term with the input data on the boundary. This cost function is minimized by a conjugate gradient algorithm in combination with a finite element discretization. We recover the non-harmonic component of the source using a priori information and an iterative algorithm for some particular classes of sources. To validate the numerical methodology, we develop synthetic examples both in circular (simple) and irregular (complex) regions. The numerical results show that the proposed methodology allows to recover the complete source and produce stable and accurate numerical solutions.

Evaluating SIFs in finite orthotropic composites from experimentally determined stress coefficients

Unlike with isotropy materials, determining stress intensity factors in orthotropic composites is complicated as they can depend on constitutive properties and principal material directions. Acknowledging the above, this paper develops a novel technique to evaluate stress intensity factors in finite, plane-stressed, orthotropic composites. Upon substantiating the numerical reliability of the approach, KI is determined experimentally in a finite tensile loaded double-edge cracked [013/905/013] graphite-epoxy composite laminate from displacement-evaluated stress coefficients. Only a single DIC-measured displacement field is need. Advantages of the method include applicability beyond a laboratory environment and to other structural shapes, loads and crack configurations, wide acceptable range of stress coefficients and highly arbitrary source locations of the measured input data. Unlike purely numerical or theoretical approaches, the method does not require knowing the external loading. This is significant as loads are often unknown in practice. Results are verified independently.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges.

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

Consequences of chemical pretreatments in particle size analysis for modelling wind erosion

The particle size distribution (PSD) of soil plays a vital role in wind erosion prediction. However, the impact of different pretreatments to remove binding agents for PSD and consequences for wind erosion modelling have not been tested. We collected 90 topsoil samples of Chernozems and Kastanozems from different test sites in Kazakhstan. Soil samples covered typical land-use types and farming methods with calcium carbonate contents reaching from 2.2 to 117.3 g kg−1 and soil organic carbon content from 11.2 to 48.7 g kg−1. Prior to particle size analysis by laser diffraction, samples were chemically pretreated separately and successively with 10% hydrochloric acid (HCl), to dissolve carbonates and 30% hydrogen peroxide (H2O2), to oxidise organic binding material. The HCl pretreatment resulted in incomplete dispersion or even aggregation due to calcium ions released by the dissolution of carbonates, while removing organic matter with H2O2 caused complete sample dispersion. The associated changes in PSD were overall minor, and only a few of our samples were assigned to a different texture class. Obtained PSD data was used to calculate texture-based properties, such as the geometric mean diameter (GMD), with a pedotransfer function. Calculated and measured input data were applied to the Single–event Wind Erosion Evaluation Program (SWEEP) to estimate potential soil losses. As a result, SWEEP's simulations showed substantial variations if the GMD is calculated based on PSD under the influence of different pretreatments. At the same time, there was no variation if the GMD was independently measured. We suggest that for standard particle size analysis of calcareous soils, pretreatment with HCl should be avoided because it might cause misleading results. Considering the variation induced by PSD analysis and resulting potential soil losses, pretreatments for laser diffraction analysis can be omitted for the investigated, silt-dominated Chernozems and Kastanozems if additional texture-based parameters are measured.

Reconstruction of the MPD system parameters at the gas kicks into well with the measured surface data in situ by the mathematical modeling

One of the main problems of managed pressure drilling (MPD) is detection and monitoring of gas kicks from the formation to the annulus. The model is proposed to estimate the parameters of the gas kick and gas-liquid flow in a well during MPD process based on measurements performed only at the surface. A gas-liquid flow is described by the kinematic wave model. The ability of gaseous hydrocarbon to dissolve in mud, when drilling on an oil based mud (OBM), is taken into account. The hydrodynamic parameters of the flow are estimated by numerically solving the equations of fluid motion jointly with measured input data: the flowrate of the mud at inlet, the flowrate of gas-liquid mixture at outlet, and the surface back pressure (SBP). The control parameters are the change in mass gas and void fraction over the well depth and on time, as well as BHP. To verify the proposed model, the calculation of gas-liquid flow parameters is conducted on base of the detailed measured data of field test during drilling of 16,000-ft deep well using a water based mud (WBM). The pattern of the gas kick into the well from the formation is reconstructed, the change in void fraction during gas rising was traced until it was kicked to the surface, and the total mass of gas entering the well is determined. The error is estimated by comparing deviations of the measured and calculated bottomhole pressure (BHP) value.

Event-triggered control for input constrained non-affine nonlinear systems based on neuro-dynamic programming

In this paper, a neuro-dynamic programming (NDP)-based event-triggered control (ETC) method is proposed for unknown non-affine nonlinear systems with input constraints. A neural network-based identifier is established with measurable input and output data to learn the unknown system dynamics. Then, a critic neural network is employed to approximate the value function for solving the event-triggered Hamilton-Jacobi-Bellman equation. Furthermore, an NDP-based ETC scheme is developed, which samples the states and updates the control law when the triggering condition is violated. Compared with the traditional time-triggered control methods, the ETC method can reduce computational burden, communication cost and bandwidth. In addition, the stability of the closed-loop system and the weight error convergence of the critic neural network are provided based on the Lyapunov’s direct method. The intersamling time is proved to be bounded by a positive constant, which excludes the Zeno behavior. Finally, two case studies are provided to verify the effectiveness of the developed ETC method.

Solving the inverse problem for determining the optical characteristics of materials

The paper describes a methodology for determining the optical and physical properties of anisotropic thin film materials. This approach allows in the future designing multilayer thin-film coatings with specified properties. An inverse problem of determining the permittivity tensor and the thickness of a thin film deposited on a glass substrate is formulated. Preliminary information on the belonging of a thin-film coating to a certain class can significantly reduce the computing time and increase the accuracy of determining the permittivity tensor over the entire investigated range of wavelengths and film thickness at the point of reflection and transmission measurement Depending on the goals, it is possible to formulate and, therefore, solve various inverse problems: o determination of the permittivity tensor and specification of the thickness of a thick (up to 1 cm) substrate, often isotropic; o determination of the permittivity tensor of a thin isotropic or anisotropic film deposited on a substrate with known optical properties. The complexity of solving each of the problems is very different and each problem requires its own specific set of measured input data. The ultimate results of solving the inverse problem are verified by comparing the calculated transmission and reflection with those measured for arbitrary angles of incidence and reflection.

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

The proposed research represents a spin-off of the Malta College of Arts, Science and Technology (MCAST) Micro-Grid (MG) project. Particularly, economic impact of Electric Vehicles (EV) integration into the MG is investigated in this paper. The MCAST MG consists of photovoltaic generation unit, a diesel generator and a battery storage system. In this paper, a Vehicle-to grid (V2G) concept is considered where utilities can profit from controlled energy trading operations according to EVs availability. EVs are categorized under different profiles considering energy and time availability of owners typical work hours. V2G energy cost is estimated based on battery energy wear due V2G extra cycling and refunded to EVs owners. As most of developed V2G studies don’t consider real world input data or/and EV battery aging cost in system modeling and evaluation, the present paper presents a reliable study as it considers a real life MG with in field measurement input data and appropriate battery degradation model. The adopted model represents a linear approximation with a minimum error value to make a suitable tradeoff of computational complexity and accuracy of obtained results. Economic assessment of the system according to the proposed energy management is performed, where results indicate that the V2G system assisted the MG operation during high electricity price period and achieved economic profit to EVs owners. According to numerical results, V2G energy trading achieved 29.90 EUR of gross selling revenues with only 4.46 EUR as battery degradation cost which makes a 16.41% average cost reduction of daily MG operation cost.

Improving cooling load prediction reliability for HVAC system using Monte-Carlo simulation to deal with uncertainties in input variables

Accurate and reliable cooling load prediction is the basis for model predictive control (MPC) of HVAC systems. Cooling load prediction process is constrained by many input variables. These input variables contain different kinds of uncertainties that should not be ignored in pursuing a reliable prediction result. However, previous load prediction methods usually improve the prediction accuracy by optimizing model structure but fail to consider the influence of input variables uncertainties. To reduce the influence of uncertainty of input variables forecast data scientifically, this paper proposes an improved cooling load prediction reliability method in which the input variables are calibrated offline with Monte-Carlo simulations and stochastic treatment before inputting them into the prediction model. A machine learning algorithm (support vector machine, SVM) is used as the prediction model. Uncertainties in weather parameters, indoor heat gains, and historical cooling load were taken into consideration. Uncertainty analysis results showed that prediction accuracy with calibrated input variables was much better (R2 = 0.9627) than those predicted with forecast input data directly (R2 = 0.9488), which was closer to load prediction obtained by using measured input data (R2 = 0.9632). The coefficient of variation (CV) value using the calibrated input data was less than 15%. The reliable cooling load prediction results are beneficial for more informed, reliable decisions on MPC application, thereby promoting energy conservation potential of HVAC systems.

Modeling of the complex refractive index and reflectivity of flat surface water over the Persian Gulf at C-band

For the microwave region of the electromagnetic spectrum, unlike visible light, the imaginary part of the refractive index can be significant, in which case it cannot be neglected. A well-known empirical model is not available for the direct calculation of the complex refractive index of seawater at the microwave region. However, based on the relationship between the refractive index and the permittivity, and by using the models provided for the seawater permittivity at the microwave frequencies the refractive index can be calculated. In this work, complex refractive index and normal reflectivity of Persian Gulf water at the C-band (5 GHz) have been calculated by using electromagnetic equations. The complex permittivity required for computing these parameters has been calculated from the Ellison's empirical model with measured input data. Calculations showed that the annual mean of basin-averaged of real and imaginary parts of the refractive index is 6.074 and 0.415, respectively. The relative spatial and temporal variability of the imaginary part of the refractive index is considerably stronger than its real counterpart. In all seasons, the reflection coefficient at the nadir in the western half of the Gulf is slightly higher than the eastern half and it has a minimum value of 0.5230 over the southeast waters in the summer and a maximum value of 0.5250 over the northwest waters of the Gulf Persian in winter.

Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

This article explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs' mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm.

Measured and simulated energy performance of OLK NZEB with heat pump and energy piles in Hämeenlinna

In this work, measured energy use of the building space heating, ventilation supply air heating, appliances and lighting is compared against simulated energy use modelled in IDA ICE. As built energy need and detailed measured input data is applied in building model calibration procedure. Calibrated building model energy performance is studied in both measured and test reference year climate conditions. Previously modelled as built plant automation and implemented control logics are compared against measured. Geothermal plant in this study consists of heat pump, solar collectors, boreholes and energy piles. Heat pump SCOP estimated by post processing according to heat pump manufacturer’s performance map is compared against measured SCOP on the monthly basis. Opinion on actual plant operation is given and energy performance improvement potential is quantified. Important parameters for successful building model calibration are presented. Building compliance with Finland NZEB requirements are assessed. The results show good match with measured energy use after the model calibration.

Understanding the performance gap in energy retrofitting: Measured input data for adjusting building simulation models

This paper focuses on exploring methods for reducing the gap between the expected and actual building energy performance by using simulation tools. The study has two purposes. The first is to quantify the relative effect of the different building parameters measured on the energy heating and cooling consumption compared with standard parameters through the adjustment of simulation models. The second is to develop an approach, based on three methods, for monitoring residential buildings, while also testing and calibrating methodologies for the simulation software. The approach developed is applied and tested in two real case studies (two apartments in two identically constructed buildings, one refurbished and the other not) in the city of Madrid, Spain. The analysis of the case studies shows that there is a four-fold difference in potential savings in energy for heating between models adjusted with standard and actual parameters. Moreover, the results reveal the significant impact of the use of actual weather data and users’ behaviour in the adjustment of simulation models and demonstrate the utility of the application of these methods.

Hybrid stress analysis of a near-surface circular hole in finite structures

The ability to stress-analyze complicated structures from recorded load-induced temperatures is demonstrated. The considered structures have a near-surface hole and subjected to a concentrated load. The complexity of the structure is simplified by conformal mapping, the traction-free condition on the boundary of the hole is analytically satisfied by analytic continuation, and the equilibrium and compatibility conditions are satisfied by means of Airy stress function in complex-variable formulation. For isotropic member that is cyclically loaded within its elastic range, the produced in-phase temperature variations are linearly proportional to the local changes in the normal stresses. Even though no recorded thermal data were used at or near to the edges, the present hybrid method simultaneously separates the load-induced temperatures into the individual stress components, determines reliably the boundary stress and hence the stress concentration, and smooths the measured input data. Unlike prior capabilities of using geometrical symmetry to simply the stress function representation, the present analysis retains all the terms in the stress functions. Therefore, the considered hybrid stress analysis approach of such complex structures extends significantly the applicability of thermoelastic stress analysis compared to prior capabilities and is considered to be the most complicated formulation of the hybrid complex-variable method to date. To support the reliability of the present hybrid method, the results were compared with finite element predictions and previous results based on Mitchell solution.