Recursive least square vehicle mass estimation based on acceleration partition

Abstract. A change in the varietal composition of wheat causes response changes in the population of the fungus, which evolves conjugately. Population studies of the causative agent of leaf rust of wheat are carried out in many countries. Comparison of populations of P. triticina is mainly carried out geographically. The purpose of the study was to analyze the dynamics of the virulence of North Caucasus Puccinia triticina populations under the influence of winter wheat varieties with different types of leaf rust resistance. Methods. The study was carried out in 2018 in the experimental field and greenhouses All-Russian Research Institute of Biological Plant Protection. Two varieties of winter wheat: Kralya and Vershina with different types of resistance to leaf rust, were used for the experiment. Cultivar Kralya had race-specific resistance; cultivar a Vershina had race-nonspecific resistance to leaf rust. Virulence analysis of the impact of each cultivar on the structure of the pathogen population based on the results of differentiation of P. triticina populations, collected from each cultivar and compared to the mixed pathogens population. As a result, it was found that the genotype of a winter wheat variety with race-specific resistance significantly affects the virulence of the P. triticina population. The frequency of virulent pathogen isolates to most lines with Lr genes has changed under the influence of driving selection. Therefore, the wide regionalization of varieties with race-specific resistance will contribute to selective pressure on the P. triticina population and the accumulation of virulent phenotypes in it. The genotype of a wheat variety with nonspecific resistance did not cause significant changes in the gene pool of the virulence of the leaf rust pathogen population. In accordance with the obtained values of the Nei index, the pathogen population collected from a variety with race-specific resistance has significant differences with the combined population (N = 0.68), in contrast to the population of P. triticina collected from the Vershina variety (N = 0.41). The results obtained confirm the importance of screening varieties by resistance types for further decision making on their placement. Varieties with race-specific resistance are best used for mosaic placement with subsequent mandatory rotation in time and space. And varieties with nonspecific resistance, which are weakly affected by all races of the pathogen and cannot be carriers of infection, can be used over large areas for a longer time, combined with varieties from various groups, including their own. Scientific novelty. There have been practically no studies on the effect of wheat varieties with different types of resistance on the virulence of rust fungi populations, although this direction is of great practical interest for both selection and varietal placement.

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles, which is prominently proved by many studies. To achieve better dynamic stable performance and higher energy recovery efficiency, an effective braking control strategy for hybrid electric buses (HEB) based on vehicle mass and road slope estimation is proposed in this paper. Firstly, the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter (EKF) and recursive least square (RLS). Secondly, the total braking torque of HEB is calculated by the sliding mode controller (SMC), which uses the information of brake intensity, whole vehicle mass, and road slope. Finally, comprehensively considering driver's braking intention and regulations of the Economic Commission for Europe (ECE), the optimal proportional relationship between regenerative braking and pneumatic braking is obtained. Furthermore, related simulations and experiments are carried out on the hardware-in-the-loop test bench. Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

This paper has presented a control system for vehicle dynamics and mass estimation. The objective of this paper is to use a single-tyre model of slip control integrated with extended Kalman filter (EKF) to estimate the states of a vehicle such as the forward velocity, wheel slip, coefficient of friction of the road surface and the mass that cannot be measured directly. In order to do this, the dynamics of a vehicle moving with a forward velocity were obtained using a single-tyre model. The dynamic equations in continuous time were transformed into their equivalent discrete time form. A two degree of freedom proportional integral and derivative (2DOFPID) control algorithm was implemented for the control loop. An estimator was designed using the extended Kalman filter algorithm to carry out the estimation based on noisy measurement of wheel rotational speed. The entire system was modeled using Matlab/Simulink blocks. Simulations were performed to determine the effectiveness of the estimator. The simulation results showed that the extended Kalman filter effectively estimated the states of a single-tyre model of a vehicle represented by a slip control system. Though the results obtained seemed promising but will be improved if the covariance matrices are calculated with adequate information and are better tuned.

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices K is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix K is selected from the control matrix set, and K is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced.

Performance, fuel economy, driving experience, and safety are the most critical attributes automotive original equipment manufacturers (OEMs) focus on meeting customer requirements. OEMs are coming up with many features content to meet the customer requirements while meeting the stringent regulations regarding emissions and safety. Advanced sensors and embedded controllers are employed to meet the above needs. Performance of these algorithms can be significantly improved if we accurately predict a few important vehicle operational parameters. Vehicle mass and road slope are two crucial parameters essential for developing and optimizing control algorithms of powertrain, brake, and advanced driver assistance systems (ADAS). This study focuses on model-based road slope and vehicle mass estimation by integrating a state estimation algorithm and a longitudinal dynamics model. Drive-train torque, vehicle velocity, and vehicle design parameters are inputs for developing a longitudinal dynamics model. Initial approximate estimates of road slope and vehicle mass are given as initialization values for the state estimation algorithm, and future estimates are obtained using recursive adaptive filtering. A comparative study was performed between Extended Kalman filter (EKF) and Recursive Least Square (RLS) estimation algorithms for the convergence rate of state variables and error with respect to the actual state.

<div class="section abstract"><div class="htmlview paragraph">A variety of vehicle controls, from active safety systems to power management algorithms, can greatly benefit from accurate, reliable, and robust real-time estimates of vehicle mass and road grade. This paper develops a parallel mass and grade (PMG) estimation scheme and presents the results of a study investigating its accuracy and robustness in the presence of various noise factors. An estimate of road grade is calculated by comparing the acceleration as measured by an on-board longitudinal accelerometer with that obtained by differentiation of the undriven wheel speeds. Mass is independently estimated by means of a longitudinal dynamics model and a recursive least squares (RLS) algorithm using the longitudinal accelerometer to isolate grade effects. To account for the influences of acceleration-induced vehicle pitching on PMG estimation accuracy, a correction factor is developed from controlled tests under a wide range of throttle levels. The estimation approach is applied to data collected while driving on public roads under a variety of driving conditions, replicating several noise factors associated with daily driving. Thresholds are developed to isolate driving events that are likely to result in quick and accurate mass estimates, and an averaging filter is then applied to these converged values. The algorithm is shown to rapidly deliver estimates within 3% of true vehicle mass.</div></div>

A receding horizon optimal control approach is proposed to control a system in such a way as to best estimate on-line its states and parameters, without significantly degrading tracking performance or violating pointwise-in-time constraints. Several automotive examples are considered to illustrate the potential of the approach. They include: vehicle mass and road grade estimation, engine wall-wetting parameter estimation, engine mapping, and estimation of the position and velocity of a moving vehicle from angle-only (passive) measurements by another vehicle.

Vehicle Mass and Road Slope Estimation Based on Interactive Multi-model（in English）

<div class="section abstract"><div class="htmlview paragraph">For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.</div><div class="htmlview paragraph">This paper introduced a mature MGE development approach which utilizes an innovative Series Kalman Filter (S-KF) structure with a 3-axis accelerometer to address the challenges above, leading to the demonstration on 3 different types of vehicles with different powertrain applications: a truck with Internal Combustion Engine (ICE) and Automated Manual Transmission (AMT) gearbox, a mid-size Plug-in Hybrid Electric Vehicle (PHEV) SUV, and an A-segment Battery Electric Vehicles (BEV) mini car. They are tested under wide range real-world driving maneuvers and road conditions. Moreover, to improve the converge time whilst keeping the same accuracy of MGE, several dedicated designed engineering features are implemented and introduced in this paper. The vehicle-based experimental result and structured development approach showed good performance and maturity of the production oriented MGE, and good efficiency and flexibility to adapt it to various applications.</div></div>

As a basic parameter, vehicle mass is closely related to economic and safety performance, etc. However, mass estimation faces challenges such as model accuracy and generalization ability. To solve these problems, a novel hybrid model-data driven vehicle mass estimation method is proposed. The model-driven method is used for calculating mass, and the data-driven method constructs a random forest-based data classifier to determine whether the data can be used; Then, by finding the relationship between data and classification results, the data structure is determined, based on the principle of increasing recall while ensuring a high precision; Finally, the proposed method is validated by monitoring data, the classification results have a high precision of more than 85%, and the error range of estimated mass is within ±5%. The excellent data classification and mass estimation capabilities enable mass monitoring for heavy-duty vehicles, which is important for global energy management, traffic safety management, etc.

Soybean resistance to Phakopsora pachyrhizi, the cause of soybean rust, has been characterized by the following three infection types: (i) immune response (IM; complete resistance) with no visible lesions, (ii) resistant reaction with reddish brown (RB) lesions (incomplete resistance), and (iii) susceptible reaction with tan-colored (TAN) lesions. Based on visual assessments of these phenotypes, single gene resistance in soybean to P. pachyrhizi has been documented, but colonization within infected tissues based on fungal DNA (FDNA) levels in different soybean genotypes had not been analyzed. The research used a quantitative polymerase chain reaction (Q-PCR) assay to compare visual disease assessment to FDNA in controlled inoculation experiments using two isolates of P. pachyrhizi. The objective of the first experiment was to compare data from digital visual disease assessment to FDNA from Q-PCR assays using digital visual disease assessment using five resistant soybean genotypes (one IM and four RB) and five susceptible genotypes (TAN). The objective of the second experiment was to quantify FDNA using Q-PCR at different time points after inoculation to determine if levels of fungal colonization differed in five soybean genotypes with different levels of resistance (one IM, two RB, and two TAN). For experiment 1, the numbers of uredinia and uredinia per lesion on four of the five resistant soybean genotypes were lower (P < 0.05) than the other six genotypes. Significant differences (P < 0.05) in FDNA concentrations were found among soybean genotypes with TAN lesions and among soybean genotypes with RB lesions. Soybean cultivar UG5 (IM phenotype) had significantly less (P < 0.05) FDNA than all of the other genotypes. Some genotypes that produced TAN lesions had significantly lower (P < 0.05) or non-significantly different FDNA concentrations compared to those genotypes that produced RB lesions. For experiment 2, the regression of FDNA on days after inoculation was significant (P < 0.01) with positive slopes for all genotypes except for UG5, in which FDNA declined over time, indicating a reduction of fungal colonization. The results of this Q-PCR FDNA screening technique demonstrates its use to distinguish different types of resistance, and could be used to facilitate the evaluation of soybean breeding populations, where precise quantification of incomplete and/or partial resistance is needed to identify and map quantitative trait loci.

RESISTANCE TO ELM DISEASE

Fusarium head blight (FHB) caused by Fusarium graminearum is a worldwide disease which has destructive effects on wheat production, resulting in severe yield reduction and quality deterioration, while FHB-infected wheat grains are toxic to people and animals due to accumulation of fungal toxins. Although impressive progress towards understanding host resistance has been achieved, our knowledge of the mechanism underlying host resistance is still quite limited due to the complexity of wheat–pathogen interactions. In recent years, disease epidemics, the resistance germplasms and components, the genetic mechanism of FHB, and disease management and control, etc., have been well reviewed. However, the resistance mechanism of FHB is quite complex with Type I, II to V resistances. In this review, we focus on the potential resistance mechanisms by linking different resistance types to multi-omics and emphasize the pathways or genes that may play significant roles in the different types of resistance. Deciphering the complicated mechanism of FHB resistance types in wheat at the integral levels based on multi-omics may help discover the genes or pathways that are critical for different FHB resistance, which could then be utilized and manipulated to improve FHB resistance in wheat breeding programs by using transgenic approaches, gene editing, or marker assisted selection strategies.

Stripe rust is best controlled by utilizing genetic resistance. Resistance to stripe rust has been studied for more than a century, but most progresses have been made during the last three decades. Two major types of resistance, all-stage resistance (ASR) and adult-plant resistance (APR) or high-temperature adult-plant (HTAP) resistance, have been characterized and used in breeding for resistant cultivars in both wheat and barley. So far, 78 permanently named, 67 temporarily designated and 327 quantitative trait loci (QTL) have been reported in various wheat varieties. The majority of these genes and QTL have been located to wheat chromosomes. Although some of the genes or QTL are the same, these numbers indicate that there are abundant resistance genes in wheat germplasm, which can be used for improving stripe rust resistance in commercially grown cultivars. Although the number is smaller, more than 50 resistance loci have been reported in barley for resistance to stripe rust, and about 30 loci have been mapped to barley chromosomes. Based on the characteristics of different types of resistance and the rich sources of resistance genes and molecular markers, we discuss different strategies for developing resistant cultivars and propose to use the combination of genes for durable APR or HTAP resistance and effective ASR to achieve more effective and sustainable control of stripe rust.

The ascomycete Zymoseptoria tritici is the causal agent of Septoria leaf blotch on wheat. Disease control relies mainly on resistant wheat cultivars and on fungicide applications. The fungus displays a high potential to circumvent both methods. Resistance against all unisite fungicides has been observed over decades. A different type of resistance has emerged among wild populations with multidrug-resistant (MDR) strains. Active fungicide efflux through overexpression of the major facilitator gene MFS1 explains this emerging resistance mechanism. Applying a bulk-progeny sequencing approach, we identified in this study a 519-bp long terminal repeat (LTR) insert in the MFS1 promoter, a relic of a retrotransposon cosegregating with the MDR phenotype. Through gene replacement, we show the insert as a mutation responsible for MFS1 overexpression and the MDR phenotype. Besides this type I insert, we found two different types of promoter inserts in more recent MDR strains. Type I and type II inserts harbor potential transcription factor binding sites, but not the type III insert. Interestingly, all three inserts correspond to repeated elements present at different genomic locations in either IPO323 or other Z.tritici strains. These results underline the plasticity of repeated elements leading to fungicide resistance in Z.tritici and which contribute to its adaptive potential. IMPORTANCE Disease control through fungicides remains an important means to protect crops from fungal diseases and to secure the harvest. Plant-pathogenic fungi, especially Zymoseptoria tritici, have developed resistance against most currently used active ingredients, reducing or abolishing their efficacy. While target site modification is the most common resistance mechanism against single modes of action, active efflux of multiple drugs is an emerging phenomenon in fungal populations reducing additionally fungicides' efficacy in multidrug-resistant strains. We have investigated the mutations responsible for increased drug efflux in Z.tritici field strains. Our study reveals that three different insertions of repeated elements in the same promoter lead to multidrug resistance in Z.tritici. The target gene encodes the membrane transporter MFS1 responsible for drug efflux, with the promoter inserts inducing its overexpression. These results underline the plasticity of repeated elements leading to fungicide resistance in Z.tritici.