Nonlinear Model Research Articles

Population pharmacokinetics of ravidasvir in adults with chronic hepatitis C virus infection and impact of antiretroviral treatment.

Ravidasvir (RDV) is a novel NS5A inhibitor that exhibits potent pan-genotypic inhibition of hepatitis C virus (HCV) replication. Sofosbuvir (SOF) plus RDV was demonstrated to be efficacious and safe in adults with active HCV infection, including those living with HIV (LWHIV), in the STORM-C-1 trial. We assessed the population pharmacokinetics (PK) of RDV in a sub-study nested within STORM-C-1 conducted in Thailand and Malaysia. SOF (400 mg) plus RDV (200 mg) was administered orally once daily for 12 weeks to adults with chronic HCV infection, but without cirrhosis and for 24 weeks to those with compensated cirrhosis. Intensive and sparse PK samples were collected at 4, 8, and 12 weeks after treatment initiation. Population PK parameters of RDV and the impact of covariates were evaluated using nonlinear mixed-effects modeling. Five hundred ninety-four participants were included, 235 (40%) had compensated cirrhosis, and 189 (32%) were LWHIV. RDV plasma concentrations were best described by a two-compartment model with first-order elimination. Oral clearance (CL/F) and volume of distribution (Vd/F) parameters were allometrically scaled on fat-free mass. Concomitant antiretroviral treatment (ART) increased RDV CL/F by 30%-60%, with efavirenz-based ART having the largest impact. Females had 16% lower RDV CL/F than males, and higher albumin levels reduced RDV central volume of distribution. While several covariates impact RDV CL/F and Vd/F, the effect on RDV exposures was not clinically relevant based on the efficacy data reported in this diverse Asian adult population. There were no meaningful drug-drug interactions in adults LWHIV on ART.

Bifurcation analysis and soliton solutions to the doubly dispersive equation in elastic inhomogeneous Murnaghan's rod.

The doubly dispersive (DD) equation finds extensive utility across scientific and engineering domains. It stands as a significant nonlinear physical model elucidating nonlinear wave propagation within the elastic inhomogeneous Murnaghan's rod (EIMR). With this in mind, we have focused on the integration of the DD model and the modified Khater (MK) method. Through the wave transformation, this model is effectively converted into an ordinary differential equation. In this paper, the goal of our work is to explore new wave solutions to the DD model by using the MK scheme. These solutions provide extremely helpful insights into the operation of the system. The three-dimensional (3D) plot and two-dimensional (2D) combined plot via the impacts of the parameters are provided for various parameters in this manuscript. We also discussed the dynamical properties of the model, which are accomplished through the bifurcation analysis, and also found the Hamiltonian function. This research makes a substantial contribution to the area by increasing our understanding of wave solutions in the DD, introducing novel investigation tools, and carrying out an in-depth investigation of the bifurcation and stability aspects of the system. As a direct result of this research, novel openings have been uncovered for further investigation and application in the various disciplines of science and engineering.

A Fast Feedforward Small-World Neural Network for Nonlinear System Modeling.

It is well-documented that cross-layer connections in feedforward small-world neural networks (FSWNNs) enhance the efficient transmission for gradients, thus improving its generalization ability with a fast learning. However, the merits of long-distance cross-layer connections are not fully utilized due to the random rewiring. In this study, aiming to further improve the learning efficiency, a fast FSWNN (FFSWNN) is proposed by taking into account the positive effects of long-distance cross-layer connections, and applied to nonlinear system modeling. First, a novel rewiring rule by giving priority to long-distance cross-layer connections is proposed to increase the gradient transmission efficiency when constructing FFSWNN. Second, an improved ridge regression method is put forward to determine the initial weights with high activation for the sigmoidal neurons in FFSWNN. Finally, to further improve the learning efficiency, an asynchronous learning algorithm is designed to train FFSWNN, with the weights connected to the output layer updated by the ridge regression method and other weights by the gradient descent method. Several experiments are conducted on four benchmark datasets from the University of California Irvine (UCI) machine learning repository and two datasets from real-life problems to evaluate the performance of FFSWNN on nonlinear system modeling. The results show that FFSWNN has significantly faster convergence speed and higher modeling accuracy than the comparative models, and the positive effects of the novel rewiring rule, the improved weight initialization, and the asynchronous learning algorithm on learning efficiency are demonstrated.

Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete

Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete

Tenofovir, emtricitabine, lamivudine and dolutegravir concentrations in plasma and urine following drug intake cessation in a randomized controlled directly observed pharmacokinetic trial to aid point-of-care testing.

Poor adherence to ART and pre-exposure prophylaxis (PrEP) can impact patient and public health. Point-of-care testing (POCT) may aid monitoring and adherence interventions. We report the pharmacokinetics of tenofovir [dosed as tenofovir disoproxil (TDF) and tenofovir alafenamide (TAF)], emtricitabine (FTC), lamivudine (3TC) and dolutegravir (DTG) in plasma and urine following drug cessation to evaluate adherence targets in urine for POCT. Subjects were randomized (1:1) to receive DTG/FTC/TAF or DTG/3TC/TDF for 15 days. Plasma and spot urine were collected on Day 15 (0-336 h post final dose). Drug concentrations were quantified using LC-MS, and non-linear mixed-effects models applied to determine drug disposition between matrices and relationship with relevant plasma [dolutegravir protein-adjusted 90% inhibitory concentration (PA-IC90 = 64 ng/mL) and minimum effective concentration (MEC = 324 ng/mL)] and urinary thresholds [tenofovir disoproxil fumarate 1500 ng/mL]. Of 30 individuals enrolled, 29 were included (72% female at birth, 90% Caucasian). Median (range) predicted time to plasma dolutegravir PA-IC90 and MEC were 83.5 (41.0-152) and 49.0 h (23.7-78.9), corresponding to geometric mean (90%) urine concentrations of 5.42 (4.37-6.46) and 27.4 ng/mL (22.1-32.7). Tenofovir in urine reached 1500 ng/mL by 101 h (58.6-205) with an equivalent plasma concentration of 6.20 ng/mL (4.21-8.18). These data support use of a urinary tenofovir threshold of <1500 ng/mL (tenofovir disoproxil fumarate-based regimens) as a marker of three or more missed doses for a POCT platform. However, due to low dolutegravir concentrations in urine, POCT would be limited to a readout of recent dolutegravir intake (one missed dose).

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

Analysis of Equilibria and Connecting Orbits in a Nonlinear Viral Infection Model

Analysis of Equilibria and Connecting Orbits in a Nonlinear Viral Infection Model

Private vs. public emergency visits for mental health due to heat: An indirect socioeconomic assessment of heat vulnerability and healthcare access, in Curitiba, Brazil.

Few studies have explored the influence of socioeconomic status (SES) on the heat vulnerability of mental health (MH) patients. As individual socioeconomic data was unavailable, we aimed to fill this gap by using the healthcare system type as a proxy for SES. Brazilian national statistics indicate that public patients have lower SES than private. Therefore, we compared the risk of emergency department visits (EDVs) for MH between patients from both healthcare types. EDVs for MH disorders from all nine public (101,452 visits) and one large private facility (154,954) in Curitiba were assessed (2017-2021). Daily mean temperature was gathered and weighed from 3 stations. Distributed-lag non-linear model with quasi-Poisson (maximum 10-lags) was used to assess the risk. We stratified by private and public, age, and gender under moderate and extreme heat. Additionally, we calculated the attributable fraction (AF), which translates individual risks into population-representative burdens - especially useful for public policies. Random-effects meta-regression pooled the risk estimates between healthcare systems. Public patients showed significant risks immediately as temperatures started to increase. Their cumulative relative risk (RR) of MH-EDV was 7.5% higher than the private patients (Q-Test 26.2%) under moderate heat, suggesting their particular heat vulnerability. Differently, private patients showed significant risks only under extreme heat, when their RR became 4.3% higher than public (Q-Test 6.2%). These findings suggest that private patients have a relatively greater adaptation capacity to heat. However, when faced with extreme heat, their current adaptation means were potentially insufficient, so they needed and could access healthcare freely, unlike their public counterparts. MH patients would benefit from measures to reduce heat vulnerability and access barriers, increasing equity between the healthcare systems in Brazil. AF of EDVs due to extreme heat was 0.33% (95%CI 0.16;0.50) for the total sample (859 EDVs). This corroborates that such broad population-level policies are urgently needed as climate change progresses.

Incorporating Fine-Grained Linguistic Features and Explainable AI into Multi-Dimensional Automated Writing Assessment

With the flourishing development of corpus linguistics and technological revolutions in the AI-powered age, automated essay scoring (AES) models have been intensively developed. However, the intricate relationship between linguistic features and different constructs of writing quality has yet to be thoroughly investigated. The present study harnessed computational analytic tools and Principal Component Analysis (PCA) to distill and refine linguistic indicators for model construction. Findings revealed that both micro-features and their combination with aggregated features robustly described writing quality over aggregated features alone. Linear and non-linear models were thus developed to explore the associations between linguistic features and different constructs of writing quality. The non-linear AES model with Random Forest Regression demonstrated superior performance over other benchmark models. Furthermore, SHapley Additive exPlanations (SHAP) was employed to pinpoint the most powerful linguistic features for each rating trait, enhancing the model’s transparency through explainable AI (XAI). These insights hold the potential to substantially facilitate the advancement of multi-dimensional approaches toward writing assessment and instruction.

Viscoelastic Model of Fiber Concrete under Dynamic Loading Considering the Effect of Initial Stresses

Statement of the problem. The objective of the study was to identify the peculiarities of the stress-strain state of fiber concrete under two-stage static-dynamic loading and to develop a variant of a nonlinear viscoelastic model of concrete reinforced with steel fiber. Results. The paper presents the results of experimental tests of fiber-concrete prisms under quasi-static, dynamic and static-dynamic loading conditions. On the basis of the analysis of experimental data it is established that at dynamic loading there is an upward shift of the boundary of macrocrack formation. A nonlinear viscoelastic model of fiber concrete is proposed and compared with experimental data, taking into account the effect of the initial stress level caused by the service load. The parametric study performed using this model shows that as the loading rate increases, a more intensive dynamic hardening of concrete is observed. Conclusions. The proposed viscoelastic model of fiber concrete allows to estimate the ultimate strength and corresponding strain under static-dynamic mode of loading due to an accidental action caused by the collapse of one of the load-bearing members of the structural system of the building.

Optimized Design of a Soft Actuator Considering Force/Torque, Bendability, and Controllability via an Approximated Structure

Abstract This paper introduces a novel design method that enhances the force/torque, bendability, and controllability of soft pneumatic actuators (SPAs). The complex structure of the soft actuator is simplified by approximating it as a cantilever beam. This allows us to derive approximated nonlinear kinematic models and a dynamical model, which is explored to understand the correlation between natural frequency and dimensional parameters of SPA. The design problem is then transformed into an optimization problem, using kinematic equations as the objective function and the dynamical equation as a constraint. By solving this optimization problem, the optimal dimensional parameters are determined. Six prototypes are manufactured to validate the proposed approach. The optimal actuator successfully generates the desired force/torque and bending angle, while its natural frequency remains within the constrained range. This work highlights the potential of using optimization formulation and approximated nonlinear models to boost the performance and dynamical properties of soft pneumatic actuators.

An integrated vendor-buyer model with sustainability and remanufacturing of returned product

Carbon emission reduction is very crucial nowadays. A proper balance is required for carbon emission reduction and burning of fossils fuels because during fossil fuel burning carbon is emitted and it is essential for industrialization. This study presents an inventory model with green technology where production of new items and remanufacturing of returned items are allowed. Remembering the present global situation, the ordering cost is assumed as variable cost which contain order cancellation and reorder cost. Online order policy is considered with quality of product and a return rate. Keeping in mind the social, economic and environmental aspects, the demand function is considered as sustainability sensitive demand. A nonlinear model is designed based on the above concepts. The model is solved both theoretically and analytically. The main motives of the work are to find the effects of ordering cost, carbon emission cost and green technology investment on the inventory model for suggesting the best policy to the inventory manager. Sensitivity analysis is performed to validate the model. The numerical results prove that ignoring ordering cost, carbon emission cost and green technology investment can lead to a false optimal solution and this can be a huge economical loss for the inventory manager. The results also prove that a sustainable model with carbon emission and green technology investment is more realistic and profitable in compare with the other existing models.

Percolation transition for random forests in $d\geqslant 3$

AbstractThe arboreal gas is the probability measure on (unrooted spanning) forests of a graph in which each forest is weighted by a factor $\beta &gt;0$ β &gt; 0 per edge. It arises as the $q\to 0$ q → 0 limit of the $q$ q -state random cluster model with $p=\beta q$ p = β q . We prove that in dimensions $d\geqslant 3$ d ⩾ 3 the arboreal gas undergoes a percolation phase transition. This contrasts with the case of $d=2$ d = 2 where no percolation transition occurs.The starting point for our analysis is an exact relationship between the arboreal gas and a non-linear sigma model with target space the fermionic hyperbolic plane $\mathbb{H}^{0|2}$ H 0 | 2 . This latter model can be thought of as the 0-state Potts model, with the arboreal gas being its random cluster representation. Unlike the standard Potts models, the $\mathbb{H}^{0|2}$ H 0 | 2 model has continuous symmetries. By combining a renormalisation group analysis with Ward identities we prove that this symmetry is spontaneously broken at low temperatures. In terms of the arboreal gas, this symmetry breaking translates into the existence of infinite trees in the thermodynamic limit. Our analysis also establishes massless free field correlations at low temperatures and the existence of a macroscopic tree on finite tori.

A modified Manson-Halford model based on improved WOA for fatigue life prediction under multi-level loading

The Manson-Halford (M-H) nonlinear cumulative damage model is widely applied for fatigue life analysis problems under multi-level loading. In this model, the influence of loading sequence on the fatigue life can be better considerer, but the loading interaction effect is ignored. An improved whale optimization algorithm (IWOA) by integrating multiple strategies is proposed. The ability of global search and local exploitation is balanced and improved through nonlinear convergence factor, adaptive weighting factors and the Cauchy reverse learning strategies. In order to fully account for loading interaction effect, loading weighting factors are introduced to modify the M-H model, and the parameters are optimized through the global search properties of IWOA. The model is evaluated on multi-level loading fatigue experimental data from five metal materials and two aluminum alloy welded joints. The results suggest that the proposed IWOA has better optimization accuracy compared to the standard whale optimization algorithm (WOA). The proposed modified M-H model has better prediction performance compared to the four traditional cumulative damage models, which can be effectively applied to multi-level loading fatigue life analysis problems under actual working conditions. The proposed model is useful for the study of fatigue life evaluation methods.

Pinecone-Inspired Humidity-Responsive Paper Actuators with Bilayer Structure

Many plant materials in nature have the ability to change their shape to respond to external stimuli, such as humidity or moisture, to ensure their survival or safe seed release. A well-known example for this phenomenon is the pinecone, which is able to open its scales at low humidity due to the specific bilayer structures of the scale. Inspired by this, we developed a novel humidity-driven actuator based on paper. This was realized by the lamination of untreated paper made from eucalyptus fibers to a paper–carboxymethyl cellulose (CMC) composite. As observed, the hygroexpansion of the composite can be easily controlled by the amount of CMC in the impregnated paper sheet, which, thus, controls the morphologic deformation of the paper bilayer. For a more detailed understanding of these novel paper soft robots, we also studied the dynamic water vapor adsorption, polymer distribution and hygroexpansion of the paper–polymer composites. Finally, we applied a geometrically nonlinear finite element model to predict the bending behavior of paper bilayers and compared the results to experimental data. From this, we conclude that due to the complexity of structure of the paper composite, a universal prediction of the hygromorphic behavior is not a trivial matter.

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood–brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (&gt;375,000 training cases) and 70% to 80% (&gt;125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.

Short-term exposure to ambient temperature and the mortality burden of suicide in Japan

Abstract Background&amp;#xD;Previous studies suggest that temperature may trigger suicide, with higher ambient temperatures associated with increased risk globally. However, quantification of the attributable suicide burden due to non-optimal temperatures, particularly across specific subgroups like sex, age, and method of suicide, is limited. This study aimed to quantify this attributable burden across Japan and assess variations in these subgroups to enhance understanding and identify high-risk populations.&amp;#xD;&amp;#xD;Methods&amp;#xD;Data on daily suicide counts and mean temperatures were collected for all 47 prefectures in Japan (1973-2015) for a comprehensive, nationally representative analysis. Using time-stratified case-crossover analyses and a distributed lag non-linear model (DLNM), we estimated temperature-suicide associations, accounting for long-term trends, seasonality, and day-of-the-week effects, and capturing both nonlinear and delayed impacts of temperature. Overall attributable fractions and fractions attributable to cold (0th-10th percentile), cool (10th-50th), warm (50th-90th), and hot (90th-100th) temperatures were computed. Subgroup analyses were conducted by sex, age (0-64 years and ≥65 years), and method of suicide (violent or nonviolent).&amp;#xD;&amp;#xD;Results&amp;#xD;Approximately 19.9% of suicides (95% empirical confidence interval (eCI): 18.8, 20.9) could be attributed to non-optimal temperatures, representing the proportion of cases that would not have occurred under optimal temperature conditions, identified at the temperature corresponding to the minimum risk of suicide. The highest fraction (9.9%, 95% eCI: 9.4, 10.4) was observed for warm temperatures (50th-90th percentile). Higher burdens were observed in females (23.7%), individuals aged ≥65 years (31.9%), and violent suicides (22.4%).&amp;#xD;&amp;#xD;Conclusions&amp;#xD;Nearly 20% of suicides in Japan are associated with non-optimal temperatures, particularly during warmer conditions. These findings highlight the importance of integrating temperature considerations into suicide prevention strategies, particularly for vulnerable populations such as the elderly. While focused on Japan, these results advocate future research to explore similar burdens globally, enhancing our understanding of environmental impacts on suicide and informing targeted public health interventions.&amp;#xD;

On optical soliton solutions of the higher-order Lakshmanan-Porsezian-Daniel model having the cubic-quintic-septic law in the presence of spatio-temporal and chromatic dispersion

Abstract The higher-order Lakshmanan–Porsezian–Daniel equation (LPDE) with the cubic-quinticseptic (CQS) law having spatiotemporal and chromatic dispersion terms (STD-CD) is examined to derive new optical soliton solutions. To accomplish this aim, we operated on&amp;#xD;a simple version of the new extended auxiliary equation method (SAEM26). The optical soliton solutions of the LPDE with CQS as well as STD-CD are constructed in detail. Moreover, 3D-surface, contour, and 2D plots are presented for the bright and periodic singular&amp;#xD;soliton solutions. Additionally, the effects of diverse model parameters on the bright soliton structure are surveyed, and these effects are displayed with 2D graphics. The findings established in this work can positively contribute to research in nonlinear optics, while the&amp;#xD;SAEM26 can be effectively applied to similar nonlinear models.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks. Predictive cruise control (PCC) provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information. In this study, a real-time implementable PCC, which simultaneously optimizes engine torque and gear shifting, is proposed for heavy-duty trucks. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control (MPC), in which the upcoming road elevation information is used. Finding the solution of the nonlinear MPC is time consuming; thus, a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method. Dynamic programming (DP) algorithm, as a global optimization algorithm, is used as a performance benchmark for the proposed solver. Simulation, hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller. The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution, with less than 1% deviation for testing roads. Moreover, the proposed co-optimization controller is implementable in a real-truck, and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9% without compromising the truck’s travel time.