Important State Variables Research Articles

Insights from utilizing data of different quality levels for simulating barley performance under Nordic conditions: The Agricultural Production Systems SIMulator model evaluation

Abstract Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality (HQ) experimental data. Despite being demanded frequently, such data are still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of HQ experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data, the prediction accuracy of Agricultural Production Systems SIMulator significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the HQ data. Process descriptions regarding, for example, early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.

Topsoil porosity prediction across habitats at large scales using environmental variables

Soil porosity and its reciprocal bulk density are important environmental state variables that enable modelers to represent hydraulic function and carbon storage. Biotic effects and their ‘dynamic’ influence on such state variables remain largely unknown for larger scales and may result in important, yet poorly quantified environmental feedbacks. Existing representation of hydraulic function is often invariant to environmental change and may be poor in some systems, particularly non-arable soils. Here we assess predictors of total porosity across two comprehensive national topsoil (0-15 cm) data sets, covering the full range of soil organic matter (SOM) and habitats (n = 1385 &n = 2570), using generalized additive mixed models and machine learning. Novel aspects of this work include the testing of metrics on aggregate size and livestock density alongside a range of different particle size distribution metrics. We demonstrate that porosity trends in Great Britain are dominated by biotic metrics, soil carbon and land use. Incorporating these variables into porosity prediction improves performance, paving the way for new dynamic calculation of porosity using surrogate measures with remote sensing, which may help improve prediction in data sparse regions of the world. Moreover, dynamic calculation of porosity could support representation of feedbacks in environmental and Earth System Models. Representing the hydrological feedbacks from changes in structural porosity also requires data and models at appropriate spatial scales to capture conditions leading to near-saturated soil conditions.Classification.Environmental Sciences.

A volatile fatty acids adaptive observer-based hierarchical optimal controller design to maximum gas production of two-stage anaerobic digestion process

Volatile fatty acids (VFA), which influence the stability and gas production, are important state variables in anaerobic digestion (AD) process. Three main components of VFA can indicate the metabolic state of AD internal environment and be considered as a better basis for AD monitoring strategies. A VFA adaptive observer (VFA-AO) is proposed to estimate unknown gas production kinetic parameters and unmeasurable states simultaneously, including the concentration of three main components of VFA. Otherwise, using the estimated information, a VFA-AO-based hierarchical optimal controller (VFA-AO-based HOC) is designed to maximize gas production rates of AD. The proposed controller is compared with two existing control algorithms. Simulation results show that, despite the concentration of pollutants in the inlet fluctuates, VFA-AO achieves a reliable states and kinetic parameters estimation, and VFA-AO-based HOC maximizes gas production.

Adapting the CROPGRO Perennial Forage model to predict growth and development of Pensacola bahiagrass

Bahiagrass (Paspalum notatum Flüggé) – a warm-season perennial grass – is one of the most important forage species for livestock production in the southeastern United States. Bahiagrass pastures are often managed extensively, without the data and tools necessary for complex management. Crop simulation models have been previously developed for warm-season grasses, enabling forecast of physiological variables and evaluation of various management scenarios. The objective of this study is to calibrate the CROPGRO Perennial Forage model (CROPGRO-PFM) for bahiagrass and to evaluate the performance of the new model across six experimental datasets. Previously developed parameters for the palisadegrass (Brachiaria brizantha cv. Xaraes) model were used initially, followed by modification of parameters more predictive of bahiagrass and its differences from palisadegrass. Major changes included calibration of plant composition, cool season temperature sensitivity, and photoperiod-induced dormancy parameters, corresponding to previous observations of bahiagrass physiology. The final adapted model resulted in effective predictive accuracy for important state variables – harvested herbage mass (RMSE = 729 kg ha−1, d-index = 0.85), above-ground biomass (RMSE = 729 kg ha−1, d-index = 0.82) and crude protein concentration (RMSE = 3.1%, d-index = 0.71). Given the wide range of experimental conditions used for model development, these results suggest that the calibrated CROPGRO-PFM model will be effective for simulating physiological variables across bahiagrass pastoral systems.

Hunting for ecological indicators: are large herbivore skeleton measures from harvest data useful proxies for monitoring?

Hunter-collected data and samples are used as indices of population performance, and monitoring programs often take advantage of such data as ecological indicators. Here, we establish the relationships between measures of skeleton size (lower jawbone length and hind-leg length) and autumn carcass mass of slaughtered individuals of known age and sex of the high Arctic and endemic Svalbard reindeer (Rangifer tarandus platyrhynchus). We assess these relationships using a long-term monitoring dataset derived from hunted or culled reindeer. The two skeleton measures were generally strongly correlated within age class. Both jaw length (R2 = 0.78) and hind-leg length (R2 = 0.74) represented good proxies of carcass mass. These relationships were primarily due to an age effect (i.e. due to growth) as the skeleton measures reached an asymptotic size at 4–6 years of age. Accordingly, strong positive correlations between skeleton measures and carcass mass were mainly evident at the young age classes (range r [0.45–0.84] for calves and yearlings). For the adults, these relationships weakened due to skeletal growth ceasing in mature animals causing increased variance in mass with age—potentially due to the expected substantial impacts of annual environmental fluctuations. As proxies for carcass mass, skeleton measurements should therefore be limited to young individuals. Although body mass is the ‘gold standard’ in monitoring large herbivores, our results indicate that skeleton measures collected by hunters only provide similar valuable information for young age classes, particularly calves and yearlings. In sum, jaw length and hind-leg length function as proxies identical to body mass when documenting the impacts of changing environmental conditions on important state variables for reindeer and other herbivores inhabiting highly variable environments.

A dataset of 10-year regional-scale soil moisture and soil temperature measurements at multiple depths on the Tibetan Plateau

Abstract. Soil moisture and soil temperature (SMST) are important state variables for quantifying the exchange of heat and water between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements are scarce on the Tibetan Plateau (TP), with even fewer are available for multiple soil depths. Tibet-Obs is such a long-term, regional-scale SMST observatory in the TP that has been established 10 years ago and includes three SMST monitoring networks, i.e. Maqu, Naqu, and Ngari (including Ali and Shiquanhe), located in the cold humid area covered by grassland, the cold semiarid area dominated by tundra, and the cold arid area dominated by desert, respectively. This paper presents a long-term (∼ 10 years) SMST profile dataset collected from the Tibet-Obs, which includes the original in situ measurements at a 15 min interval collected between 2008 and 2019 from all the three networks and the spatially upscaled data (SMups and STups) for the Maqu and Shiquanhe networks. The quality of the upscaled data is proved to be good, with errors that are generally better than the measured accuracy of adopted SMST sensors. Long-term analysis of the upscaled SMST profile data shows that the amplitudes of SMST variations decrease with increasing soil depth, and the deeper soil layers present a later onset of freezing and an earlier start of thawing and, thus, a shorter freeze–thaw duration in both the Maqu and Shiquanhe networks. In addition, there are notable differences between the relationships of SMups and STups under freezing conditions for the Maqu and Shiquanhe networks. No significant trend can be found for the SMups profile in the warm season (from May to October) for both networks that is consistent with the tendency of precipitation. A similar finding is also found for the STups profile and air temperature in the Shiquanhe network during the warm season. For the cold season (from November to April), a drying trend is noted for the SMups above 20 cm in the Maqu network, while no significant trend is found for those in the Shiquanhe network. Comparisons between the long-term upscaled data and five reanalysis datasets, including the ECMWF reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), Global Land Data Assimilation System version 2 Catchment Land Surface Model (GLDAS-2.1 CLSM), GLDAS-2.1 Noah, and GLDAS-2.1 variable infiltration capacity (GLDAS-2.1 VIC), indicate that none of the current model-based products can reproduce the seasonal variations and interannual trend changes in the measured SMST profile dynamics in both networks. All the products underestimate the STups at every depth, leading to an earlier onset of freezing and a later onset of thawing, which essentially demonstrates that the current models are not able to adequately simulate winter conditions on the TP. In short, the presented dataset would be valuable for evaluation and improvement in long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/20141567.v1 (Zhang et al., 2022).

A Modified SWAT Model to Simulate Soil Water Content and Soil Temperature in Cold Regions: A Case Study of the South Saskatchewan River Basin in Canada

Soil water content (SWC) and soil temperature are important hydrologic state variables. Accurate model simulation is critical in hydrologic regimes in cold regions dominated by spring snowmelt. In this study, we developed a combined physically-based soil temperature and energy-balance rain-on-snow (ROS) module for the Soil and Water Assessment Tool (SWAT) model and applied it to the South Saskatchewan River Basin (SSRB). We calibrated the SWAT base (SWAT-B) model and the SWAT modified (SWAT–M) model using daily measured soil temperature and SWC by hydrological response unit (HRU) for the years 2015 to 2020. The results of sensitivity analysis using the SUFI-2 technique in SWAT-CUP indicated that eight parameters have the most significant (p < 0.5) effect on streamflow, soil moisture, and snowmelt. Statistics for the SWAT-B and SWAT-M streamflow models revealed that the new module improved the Nash-Sutcliffe efficiency (NSE) from 0.39 to 0.71 and 0.42 to 0.76 for calibration and validation, respectively. The statistics for SWAT-simulated daily SWC showed that the measured data were a better fit with SWAT-M versus the SWAT-B output. Furthermore, SWAT-B values exceeded SWAT-M output and field measurements, and thus, the range of SWAT-M results was a better fit with observations. SWAT-B tended to underestimate soil temperature in the cold season, while SWAT-M significantly improved soil temperature simulation for winter. This new SWAT module simulated freeze-thaw cycles and captured the influence of snow cover on surface soil ice-water content. Spatial analysis of SWC and soil temperature across the SSRB showed that the SWAT-M model predicted more SWC and lower soil temperature in the western part of SSRB than SWAT-B, with higher soil temperature and lower SWC in the eastern region.

Experimental methods in chemical engineering: Pressure

AbstractPressure and temperature are the most important state variables for monitoring physicochemical processes to detect deviations that might lead to explosions and to verify levels, flow rate, and solids/gas hold‐up. Pressure fluctuations in multi‐phase systems identify regime changes and flow anomalies. Pressure signals are the first indicator of a process upset and are tied into distributed control systems (DCS) to sound alarms when they drift to high or low and activate safety interlocks in the case of high, high‐high, low, and low‐low conditions. To maximize the information, it requires that pressure gauges (transducers) are installed and calibrated precisely. Pressure measuring devices include manometers, aneroid devices like bellows and Bourdon gauges, and electronic instruments—piezoresistive, piezoelectric, and capacitive. The electronic elements have the advantage of higher precision and faster response times to measure fluctuations. The Bourdon gauges are standard equipment for pressure regulators and are mounted on the exterior of vessels and pipes to facilitate visual inspections. Over 2 million articles indexed by the Web of Science Core collection mention pressure, and in 2021, chemical engineering ranks had over 7000 articles—only multidisciplinary material sciences and energy and fuels had more. A bibliometric analysis identified five research clusters: temperature, combustion, and kinetics; separation, membranes, and energy efficiency; carbon dioxide (capture and storage), water, and thermodynamics; methane, adsorption, and transport phenomena (e.g., diffusion and permeability); and modelling, optimization, and computational fluid dynamics (CFD).

The government spending multiplier at the zero lower bound: International evidence from historical data

SummaryBased on a large historical panel dataset, this paper provides evidence that the government spending multiplier can be significantly higher when interest rates are at or near the zero lower bound (ZLB). We estimate multipliers that are as high as 1.5 during ZLB episodes but small and statistically indistinguishable from zero during normal times. Our results are robust to different definitions of ZLB episodes, alternative ways of identifying government spending shocks, controlling for the exchange rate regime, and other potentially important state variables. In particular, we show that the difference in multipliers is not driven by multipliers being higher during periods of economic slack.

Prediction of long-term inter-seasonal variations of streamflow and sediment load by state-space model in the Loess Plateau of China

Streamflow and sediment load exporting from catchments usually show high inter-variability caused by climate variability and land surface changes. It is challenge to predict monthly or daily streamflow and sediment load using simply statistical methods. The state-space model is an effective tool for quantifying localized variations, and it only needs some easily obtained climate and land surface variables. However, there is no state-space model developed to predict the long-term variations of monthly streamflow, sediment load and suspended sediment concentration. In this study, fourteen main catchments in the Loess Plateau of China were chosen, and these catchments experienced abrupt reductions of annual streamflow (Q), specific sediment yield (SSY) and suspended sediment concentration (SSC) during the past five decades. The state-space model was developed to predict monthly Q, SSY and SSC during 1982–2011 by precipitation, potential evapotranspiration and normalized difference vegetation index. The results indicated that the monthly Q, SSY and SSC in summer season decreased significantly in nearly all the catchments (p < 0.01). The periodicity of monthly Q, SSY and SSC reduced, and especially the periodicity was not detected after 2003. The precipitation and previous Q were the two main state variables to estimate monthly Q with transition coefficients of 0.56 and 0.25, respectively, whereas Q and previous SSC were the most important state variables to estimate monthly SSY and SSC with transition coefficients of 0.50 and 0.34, respectively. The state-space model was satisfactory to simulate monthly Q, SSY and SSC with Nash-Sutcliffe model efficiency (NSE) over 0.85 in model calibration and validation. Furthermore, the model performance was best for predicting monthly Q, followed by predicting monthly SSY and SSC with the average NSE of 0.90, 0.89 and 0.87, respectively. The reductions of monthly streamflow and sediment load were mainly derived by soil and water conservation measures (SWCMs) and especially check-dams. The catchment runoff and sediment coefficient decreased significantly and linearly with the percentages of area affected by SWCMs (p < 0.01). This study indicates that the state-space model is a useful tool to predict streamflow and sediment load time series with the hydrological and land surface informations.

Modelling flow and sediment transport change on the ecosystem in the large fresh water lake-Poyang Lake

River connected lakes exchange substances such as water and sediment with the rivers frequently, which means the ecosystem is significantly influenced by the relationship between the lake and the rivers. Affected by the construction and operation of the upstream reservoirs and the evolution of the climate conditions, the relationship between the lake and the rivers connected to Poyang Lake (PYL) has changed remarkably. The changed relationship has a direct impact on the inflow and outflow conditions of the water and sediment, which are most important state variables and driving variables in the aquatic ecosystem, specifically, the inorganic environment of an aquatic ecosystem is mainly composed of water and various forms of sediment and the transport of water and sediment drives the transport of nutrients and biological elements and forms the aquatic habitat. To study the impact of the flow and sediment transport change on the ecosystem in PLY, this study builds the food web model and simulates the evolution of the aquatic ecosystem by virtue of AQUATOX, which is a comprehensive eco-model developed by Environmental Protection Agency. The results illustrate there is a degeneration of the ecosystem in PYL when the sediment transport is decreased significantly, and the water discharge of Hukou is increased in dry season. In the 30 years’ simulation period, the biomass concentration of chlorophyll a increased significantly in summer and autumn, and the biomass concentration of benthos decreased significantly from 2012 to 2030.

What Can Machine Learning Tell Us About Intraday Price Patterns in a Frontier Stock Market?

Quite a lot. On the one hand, it enables us to classify intraday patterns into 6 unique classes and to show how each class is related to several important market state variables. On the other hand, it enables us to identify the relevant set of variables and define a better model of the drivers of intraday patterns in a frontier stock market. Overall, our results show that intraday patterns in returns in the frontier stock market of Romania are mostly the result of risk, information flows, and spillover effects from more developed international markets. However, we find that low market efficiency and investor behavior also have a significant contribution. Among others, we identify signs of overreaction to information, irrational exuberance and “making the close” practices by different types of investors.

A Soft Sensor Approach Based on an Echo State Network Optimized by Improved Genetic Algorithm.

In the process of fault diagnosis and the health and safety operation evaluation of modern industrial processes, it is crucial to measure important state variables, which cannot be directly detected due to limitations of economy, technology, environment and space. Therefore, this paper proposes a data-driven soft sensor approach based on an echo state network (ESN) optimized by an improved genetic algorithm (IGA). Firstly, with an ESN, a data-driven model (DDM) between secondary variables and dominant variables is established. Secondly, in order to improve the prediction performance, the IGA is utilized to optimize the parameters of the ESN. Then, the immigration strategy is introduced and the crossover and mutation operators are changed adaptively to improve the convergence speed of the algorithm and address the problem that the algorithm falls into the local optimum. Finally, a soft sensor model of an ESN optimized by an IGA is established (IGA-ESN), and the advantages and performance of the proposed method are verified by estimating the alumina concentration in an aluminum reduction cell. The experimental results illustrated that the proposed method is efficient, and the error was significantly reduced compared with the traditional algorithm.

The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling

Water level or water surface elevation (WSE) is an important state variable of rivers, lakes, and wetlands. Hydrodynamic models of rivers and streams simulate WSE and can benefit from spatially distributed WSE observations, to increase model reliability and predictive skill. This has been partially addressed by satellite radar altimetry, but satellite altimetry is unable to deliver useful data for small rivers. To overcome such limitations, we deployed a radar altimetry system on an unmanned aerial vehicle (UAV), to map spatially distributed WSE. We showed that UAV altimetry can provide observations of WSE with a very high spatial resolution (ca. 0.5 m) and accuracy (ca. 3 cm), in a time-saving and cost-effective way. Furthermore, we investigated the value of this dataset for the calibration and validation of hydrodynamic models. Specifically, we introduced spatially distributed roughness parameters in a hydrodynamic model and estimated these parameters, using the observed WSE profiles along the stream as input. A case study was conducted in the Åmose stream, Denmark. The results showed that UAV-borne WSE can identify significant variations of the Manning–Strickler coefficients, along this small and highly vegetated stream and over time. Moreover, the model performed extremely well using distributed roughness coefficients, but it could not reproduce WSE satisfactorily using uniform roughness. We concluded that distributed roughness coefficients should be considered, especially for small vegetated rivers, to improve model performance, both locally and globally. Spatially distributed parameterizations of the effective channel roughness could be constrained with UAV-borne WSE. This study demonstrated for the first time that UAV-borne WSE can help to understand the variations of hydraulic roughness, and can support efficient river management and maintenance.

State estimation of distributed electric vehicle based on robust adaptive UKF

The key to achieving active safety and automatic driving is accurate parameters of the vehicle state. The unscented Kalman filter (UKF) algorithm will seriously affect the vehicle state’s estimation accuracy if the noise of measurement is high or the covariance of noise does not suit. A robust adaptive UKF algorithm based on fault detection mechanism is proposed to estimate the state of distributed drive electric vehicle. The algorithm uses the residual vector of observation noise to identify whether there is a fault in the system, determines whether the observation noise covariance and the process noise covariance need to be adjusted adaptively according to the statistical function, and updates the covariance based on the weight factors. A robust adaptive UKF estimator is designed to estimate three important state variables: longitudinal speed, lateral speed, and sideslip angle. The algorithm is eventually tested based on the co-simulation of CarSim and MATLAB/Simulink. The results showed that the proposed robust adaptive UKF algorithm can significantly reduce the estimation error of three state variables, and is better than the standard accuracy and robustness of the UKF algorithm. This lays an important foundation for advanced driving assistance systems and precise automatic driving movement control.

Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation.

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters; (2) the vector space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates; and (6) an estimate of the initial conditions. Three glucose fluxes are then estimated, namely; glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization; and other important state variables. The simulation results show that the technique is capable of estimating the glucose fluxes with high accuracy, even for complex meal scenarios. The experimental results indicate that the technique is capable of reproducing the triple tracer measurements for three T1DM undergoing the triple tracer protocol while estimating the missing measurements for a certain model parameter selection.

IDENTIFICATION OF THE MASS INERTIA MOMENT IN AN ELECTROMECHANICAL SYSTEM BASED ON WAVELET–NEURAL METHOD

This paper presents the results of testing of a complex electromechanical system model. These results have been obtained for accepted in simulations the method of identifying an inertia moment of reduced masses on shaft of induction motor drive during the changes of a backlash zone width. The effectiveness of correct diagnostic conclusions enables coefficients analysis of testing signals wavelet expansion as well as weights of a supervised learning neural network. The earlier fault detection of five important state variables, which describe physical quantities of chosen complex electromechanical system has been verified for its correctness during the backlash zone width monitoring in the early stage of its gradual rise. The proposed here algorithm with mass inertia moment changes has proved to be an effective diagnostic method in the area of system changeable dynamic conditions and this has been shown in the resulting changes of backlash zone width.

Adaptive prescribed performance control of half-car active suspension system with unknown dead-zone input

Abstract This paper proposes a novel adaptive control scheme for half-car active suspension system (HCASS) with unknown dead-zone input. Under the proposed control framework, the overall HCASS is divided into two subsystems. The proposed controller is designed based on the first subsystem, and the second subsystem is regarded as zero dynamics of HCASS. For the first subsystem, a new robust adaptive strategy is first constructed to compensate the adverse effects of unknown dead-zone input nonlinearities. Further, in order to ensure some important state variables within the given restrictive conditions all the time, a novel prescribed performance control strategy (PPC, for short) is designed. For the second subsystem, the corresponding stability analysis of zero dynamics is presented. Finally, the solution of the resulting closed-loop system is ensured to be uniformly ultimately bounded, and the effectiveness of the proposed approach is illustrated by a strict and complete simulation analysis.

Reduced‐Order Observers for Nonlinear State Estimation in Flexible Multibody Systems

Modern control schemes adopted in multibody systems take advantage of the knowledge of a large set of measurements of the most important state variables to improve system performances. In the case of flexible‐link multibody systems, however, the direct measurement of these state variables is not usually possible or convenient. Hence, it is necessary to estimate them through accurate models and a reduced set of measurements ensuring observability. In order to cope with the large dimension of models adopted for flexible multibody systems, this paper exploits model reduction for synthesizing reduced‐order nonlinear state observers. Model reduction is done through a modified Craig‐Bampton strategy that handles effectively nonlinearities due to large displacements of the mechanism and through a wise selection of the most important coordinates to be retained in the model. Starting from such a reduced nonlinear model, a nonlinear state observer is developed through the extended Kalman filter (EKF). The method is applied to the numerical test case of a six‐bar planar mechanism. The smaller size of the model, compared with the original one, preserves accuracy of the estimates while reducing the computational effort.