The association of preconceptional folic acid supplementation with gestational age and preterm birth risk in twin pregnancies: a prospective cohort study in China.

PURPOSE: This study aims to explore the association of maternal preconceptional folic acid (FA) supplementation with gestational age and preterm birth in twin pregnancies, and whether the association varies by chorionicity or conception mode. METHODS: From November 2018 to December 2021, the information of FA supplementation and pregnancy outcomes were collected in twin pregnant women. The linear regression models and the logistic regression were used to test the association of preconceptional FA supplementation with gestational age at delivery and preterm birth and premature rupture of membranes (PROM). RESULTS: A total of 416 twin pregnancies were included. Compared with no use in twins, maternal preconceptional FA use was associated with a 0.385-week longer gestational age (95% CI 0.019-0.751) and lower risk of preterm birth < 36 weeks (adjusted OR 0.519; 95% CI 0.301-0.895) and PROM (adjusted OR 0.426; 95% CI 0.215-0.845). The protective effect on preterm birth < 36 weeks and PROM is similar whether taking FA supplements alone or multivitamins. However, the associations varied by chorionicity and conception mode of twins or compliance with supplementation. The positive associations between preconceptional FA use and gestational age only remained significant among twins via assisted reproductive technology or dichorionic diamniotic twins. Significant protective effects on preterm birth < 36 weeks and PROM were only found among women who took FA at least 4 times a week before conception. CONCLUSION: Maternal preconceptional FA supplementation was associated with longer gestation duration and lower risk of preterm birth < 36 weeks and PROM in twin pregnancies. To improve the success of their pregnancies, reproductive women should start taking FA supplements well before conception and with good compliance.

Correlation analysis of adverse outcomes for the selective reduction of twin pregnancies.

BACKGROUND: Due to the extensive development of assisted reproductive technology, the number of twin pregnancies has increased significantly over recent decades. Twin pregnancy is the most representative type of multiple pregnancies and is associated with high infant morbidity and mortality. Perinatal complications of twin pregnancy are also markedly increased compared with those of single pregnancy. Transabdominal selective reduction (SR) is a remedial intervention. This study aimed to research the adverse outcomes of transabdominal selective reduction of twin pregnancy and the correlation between the reduction week and pregnancy outcomes. OBJECTIVE: The purpose of this study was to examine the adverse outcomes of the transabdominal selective reduction of twin pregnancy and the correlation between the reduction week and pregnancy outcomes. METHODS: A retrospective cohort study of the transabdominal reduction of twin pregnancy was conducted in a single prenatal diagnosis medical centre from September 2012 to October 2020. According to chorionicity, women with twin pregnancies were divided into 2 groups: dichorionic (DC) twin pregnancies and monochorionic (MC) twin pregnancies. Women with DC twin pregnancies underwent potassium chloride reduction, and those with MC twin pregnancies underwent radiofrequency ablation (RFA). The reduction indications included pregnancy complications, foetal abnormalities, and maternal factors. The perinatal outcomes of different chorionic twins after reduction were analysed. Each foetus with an adverse outcome was included. The relative relationship between the reduction weeks and delivery weeks of twins was examined by correlation analysis. RESULTS: A total of 161 women were included in this study. A total of 112 women had DC twin pregnancies, and 49 women had MC twin pregnancies. Preterm delivery rates were significantly higher in the MC twin reduction group than in the DC twin reduction group prior to 37 weeks (53.1% vs. 29.5%, P = 0.004). The mean gestational age at delivery of the foetuses in the DC twin group that underwent SR was significantly older than that of those in the MC twin group that underwent SR (36.9 ± 4.0 vs. 33.5 ± 6.6 weeks, P = 0.001). The number of DC twins that underwent SR and were delivered after 37 weeks was obviously greater than that of the MC twins that underwent SR (70.5% vs. 46.9%, P = 0.004). The foetal survival rate was 95.5% in the DC twin reduction group and 77.6% in the MC twin reduction group. If the indication of TTTS was not included, there was no significant difference in the foetal survival rate of the DC and MC twin reduction groups (95.5% vs. 86.2%, P = 0.160). Cotwin death 1 week after reduction was greater in the MC group (6.1% vs. 0%, P = 0.027). Compared to other indications, this finding indicated that a significantly lower proportion of women remained undelivered after selective reduction with the indication of TTTS. There was a significant negative correlation between the reduction weeks and delivery weeks of the two groups (P < 0.01), and the best opportunity for reduction was before 22 weeks of gestation. CONCLUSION: These findings highlighted an obviously negative correlation between the reduction week and delivery week. The transabdominal selective reduction of twin pregnancy should be considered for a lower rate of miscarriage or premature delivery if the reduction week takes place earlier in pregnancy. The rate of preterm delivery was the lowest when transabdominal selective reduction was completed before 22 weeks of gestation. Compared with other RFA indications, a higher rate of premature delivery was shown for MC twins with a reduction indication of TTTS. TTTS with sIUGR might be one of the reasons for the adverse outcomes of reduction for MC twin pregnancy.

Trends of central obesity and associations with nutrients intake and daily behaviors among women of childbearing age in China.

BACKGROUND: Obesity among women of childbearing age has becoming an important public health concern. We aimed to describe the trends of central obesity among Chinese women of childbearing age from 2004 to 2011 and to examine its associations with nutrients intake and daily behaviors. METHODS: Longitudinal data were derived from the China Health and Nutrition Survey. Participants consisted of 2481 women aged 15-44 years old. WC (Waist circumference) and WHtR (Waist to height ratio) were adopted as indicators of central obesity. Generalized linear mixed model was performed to analyze the associations of nutrients intake and daily behaviors with central obesity. RESULTS: From 2004 to 2011, the prevalence of central obesity among Chinese women of childbearing age increased from 21.6 to 30.7% (WC as indice) or from 22.8 to 32.6% (WHtR as indice) (both p < 0.001). Protein intake above the AMDR (Acceptable macronutrient distribution range) (OR = 1.21, 95% CI 1.05-1.39, p < 0.01) and non-participation in LTPA (Leisure time physical activity) (OR = 1.45, 95% CI 1.17-1.80, p < 0.001) were risk factors for high WC, and the latter was also associated with high WHtR (OR = 1.36, 95% CI 1.10-1.67, p < 0.01). For those women who had high WC & high WHtR, the impacts of protein intake and LTPA became stronger, especial LTPA (OR = 1.53, 95% CI 1.21-1.94, p < 0.001). Age-stratified analyses found that non-participation in LTPA was key factor for central obesity in 15-34 age group, while protein intake above the AMDR was pronounced in the 35-44 age group. CONCLUSIONS: Non-participation in LTPA and protein intake above the AMDR were significant contributors of central obesity, which could be intervention targets to deal with the growing trend of central obesity among women of childbearing age.

Effect of maternal sleep, physical activity and screen time during pregnancy on the risk of childhood respiratory allergies: a sex-specific study.

BACKGROUND: Early life exposure in the uterus had a long-term effect on children's health. As the prevalence of allergies is increasing with a remarkable sex difference, very few studies have traced back to their early origins. We sought to investigate if maternal behavioral exposure, herein sleep, physical activity, and screen time during pregnancy is associated with childhood respiratory allergies. The sex difference would be examined. METHODS: Six thousand two hundred thirty-six mother-child pairs from Shanghai Children Allergy Study (SCAS) were enrolled, The International Study of Asthma and Allergies in Childhood questionnaire was adopted to evaluate respiratory allergic diseases. RESULTS: 14.6, 16.2, and 21.0% of children had asthma, wheeze, and allergic rhinitis, respectively. Maternal short sleep duration, lack of physical activity, and too much screen exposure during pregnancy could increase the risk of childhood respiratory allergies, however, the significance was found only in males. Moreover, a dose-response trend was clearly shown, any two of the three combined could increase the risk (OR,1.921; 95% CI,1.217-3.033), and the coexistence of all three further amplified the risk (OR,2.412; 95% CI,1.489-3.906). The findings can be verified in allergen test subgroup and each single type of respiratory allergies in most cases. CONCLUSIONS: Maternal unhealthy behaviors during pregnancy could increase the risk of childhood respiratory allergies with a dose-response pattern. Males were more susceptible to the association. The identification of modifiable maternal risk behaviors lies in the emphasis of intervention in early life to face up increasing childhood allergies.

Periconceptional use of cod liver oil, a vitamin D source, could decrease the risk of CHD in offspring.

OBJECTIVE: To explore if there is association between vitamin D supplementation through cod liver oil ingestion around the periconceptional period and the risk of developing severe CHD in offspring. Furthermore, we would examine the interaction between vitamin D and folic acid supplementation in the association. METHODS: A case-control study was conducted in Shanghai Children's Medical Center, in which, a total of 262 severe CHD cases versus 262 controls were recruited through June 2016 to December 2017. All children were younger than 2 years. To reduce potential selection bias and to minimise confounding effects, propensity score matching was applied. RESULTS: After propensity score matching, vitamin D supplementation seemed to be associated with decreased odds ratio of severe CHD (odds ratio = 0.666; 95% confidence intervals: 0.449-0.990) in the multivariable conditional logistic analysis. Furthermore, we found an additive interaction between vitamin D and folic acid supplementation (relative excess risk due to interaction = 0.810, 95% confidence intervals: 0.386-1.235) in the association. CONCLUSION: The results suggested that maternal vitamin D supplementation could decrease the risk of offspring severe CHD; moreover, it could strengthen the protective effect of folic acid. The significance of this study lies in providing epidemiological evidence that vitamin D supplementation around the periconceptional period could be a potential nutritional intervention strategy to meet the challenge of increasing CHD.

Associations between maternal social support and stressful life event with ventricular septal defect in offspring: a case-control study.

BACKGROUND: Previous studies suggested that maternal subjective feeling of stress seemed to be involved in the incidence of congenial heart disease in offspring. To better understand the findings, our study would discuss the relationships of maternal exposure to stressful life event and social support, which are more objective and comprehensive indicators of stress, around periconceptional period with the risk of ventricular septal defect (VSD), the most popular subtype of congenital heart disease. METHODS: A hospital-based case-control study was conducted through June, 2016 to December, 2017. We collected maternal self-reports of 8 social support questions in 3 aspects and 8 stressful life events among mothers of 202 VSD cases and 262 controls. Social support was categorized into low, medium high, and high (higher is better), and stressful life event was indexed into low, medium low, and high (higher is worse). Logistic regression models were applied to estimate adjusted odds ratios and 95% confidence intervals (95% CI). RESULTS: The adjusted odds ratio of high stressful life event was 2.342 (95% CI: 1.348, 4.819) compared with low stressful life event. After crossover analysis, compared with low event & high support, the adjusted odds ratio of low event & low support, high event & high support, and high event & low support were 2.059 (95% CI: 1.104, 3.841), 2.699 (95% CI: 1.042, 6.988) and 2.781 (95% CI: 1.033, 7.489), respectively. CONCLUSIONS: In summary, we observed an increased risk of VSD when pregnant women exposed to stressful life events, however, social support could, to some extent, reduce the risk of stressful life event.

Research about DoS Attack against ICPS.

This paper studies denial-of-services (DoS) attacks against industrial cyber-physical systems (ICPSs) for which we built a proper ICPS model and attack model. According to the impact of different attack rates on systems, instead of directly studying the time delay caused by the attacks some security zones are identified, which display how a DoS attack destroys the stable status of the ICPS. Research on security zone division is consistent with the fact that ICPSs' communication devices actually have some capacity for large network traffic. The research on DoS attacks' impacts on ICPSs by studying their operation conditions in different security zones is simplified further. Then, a detection method and a mimicry security switch strategy are proposed to defend against malicious DoS attacks and bring the ICPS under attack back to normal. Lastly, practical implementation experiments have been carried out to illustrate the effectiveness and efficiency of the method we propose.

Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion.

In most practical applications, the tracking process needs to update the data constantly. However, outliers may occur frequently in the process of sensors' data collection and sending, which affects the performance of the system state estimate. In order to suppress the impact of observation outliers in the process of target tracking, a novel filtering algorithm, namely a robust adaptive unscented Kalman filter, is proposed. The cost function of the proposed filtering algorithm is derived based on fading factor and maximum correntropy criterion. In this paper, the derivations of cost function and fading factor are given in detail, which enables the proposed algorithm to be robust. Finally, the simulation results show that the presented algorithm has good performance, and it improves the robustness of a general unscented Kalman filter and solves the problem of outliers in system.

Differential Mobility Spectrometry Coupled with Multiple Ion Monitoring in Regulated LC-MS/MS Bioanalysis of a Therapeutic Cyclic Peptide in Human Plasma.

A differential mobility spectrometry (DMS) in combination with a multiple ion monitoring (MIM) method was developed and validated for quantitative LC-MS/MS bioanalysis of pasireotide (SOM230) in human plasma. Pasireotide, a therapeutic cyclic peptide, exhibits poor collision-induced dissociation (CID) efficiency for multiple reaction monitoring (MRM) detection. Therefore, in an effort to increase the overall sensitivity of the assay, a DMS-MIM approach was explored. By selecting the most abundant doubly charged precursor ion in both the Q1 and Q3 of the mass analyzer in MIM and combining the DMS capability to significantly reduce the high matrix/chemical background noise, this new LC-DMS-MIM method overcomes the sensitivity challenge in the typical MRM method due to poor CID fragmentation of the analyte. Human plasma was spiked with pasireotide with concentrations in the range 0.01-50 ng/mL. Weak cation-exchange solid-phase extraction was employed for sample preparation. The sample extracts were analyzed with a SCIEX QTRAP 6500 system equipped with an ESI source and DMS device. The separation voltage and compensation voltage of the DMS and other parameters of the MS system were optimized to maximize signal responses. The performance of the LC-DMS-MIM assay for quantitative analysis of pasireotide in human plasma was evaluated and compared to those obtained via LC-MRM and LC-MIM without DMS. Overall, the assay sensitivity with DMS-MIM was approximately 5-fold better than that observed in MRM or MIM without DMS. The assay was validated with accuracy (% bias) and precision (% CV) of the QC results at eight concentration levels (0.01, 0.02, 0.05, 0.15, 0.3, 1.5, 15, and 37.5 ng/mL) evaluated ranging from -4.8 to 5.0% bias and 0.7 to 8.6% CV for the intraday and interday runs. The current LC-DMS-MIM workflow can be expanded to quantitative analysis of other molecules that have poor fragmentation efficiency in CID.

Reinforcement Learning-Based Model Predictive Control for Discrete-Time Systems.

This article proposes a novel reinforcement learning-based model predictive control (RLMPC) scheme for discrete-time systems. The scheme integrates model predictive control (MPC) and reinforcement learning (RL) through policy iteration (PI), where MPC is a policy generator and the RL technique is employed to evaluate the policy. Then the obtained value function is taken as the terminal cost of MPC, thus improving the generated policy. The advantage of doing so is that it rules out the need for the offline design paradigm of the terminal cost, the auxiliary controller, and the terminal constraint in traditional MPC. Moreover, RLMPC proposed in this article enables a more flexible choice of prediction horizon due to the elimination of the terminal constraint, which has great potential in reducing the computational burden. We provide a rigorous analysis of the convergence, feasibility, and stability properties of RLMPC. Simulation results show that RLMPC achieves nearly the same performance as traditional MPC in the control of linear systems and exhibits superiority over traditional MPC for nonlinear ones.

Event-Triggered Mechanism-Based Discrete-Time Nash Equilibrium Seeking for Graphic Game With Outlier-Resistant ESO.

In this article, we consider a discrete-time Nash equilibrium (NE) seeking problem for graphic game subject to disturbances. For the first-order dynamics, the discrete-time outlier-resistant extended state observer (ESO)-based game strategy is proposed to enable the players to estimate the disturbances under effect of anomaly measurements and then compensate them. An event-triggered mechanism is applied between adjacent players to reduce the frequency of communication. The convergence of the outlier-resistant ESO and control strategy is presented. Moreover, the upper bound of ϵ -NE solution deviating from the unique point of nominal system is given analytically. Then, the addressed issues are extended to high-order game systems. The NE seeking-based control strategy for each player is designed such that the equilibrium point converges to the ϵ -NE which is also analytically calculated. Finally, in order to verify the effectiveness of the proposed game strategy, an example of satellite system is given.

An Efficient Robotic Pushing and Grasping Method in Cluttered Scene.

Pushing and grasping (PG) are crucial skills for intelligent robots. These skills enable robots to perform complex grasping tasks in various scenarios. These PG methods can be categorized into single-stage and multistage approaches. Single-stage methods are faster but less accurate, while multistage methods offer high accuracy at the expense of time efficiency. To address this issue, a novel end-to-end PG method called efficient PG network (EPGNet) is proposed in this article. EPGNet achieves both high accuracy and efficiency simultaneously. To optimize performance with fewer parameters, EfficientNet-B0 is used as the backbone of EPGNet. Additionally, a novel cross-fusion module is introduced to enhance network performance in robotic PG tasks. This module fuses and utilizes local and global features, aiding the network in handling objects of varying sizes in different scenes. EPGNet consists of two branches dedicated to predicting PG actions, respectively. Both branches are trained simultaneously within a Q -learning framework. Training data is collected through trial and error, involving the robot performing PG actions. To bridge the gap between simulation and reality, a unique PG dataset is proposed. Additionally, a YOLACT network is trained on the PG dataset to facilitate object detection and segmentation. A comprehensive set of experiments is conducted in simulated environments and real-world scenarios. The results demonstrate that EPGNet outperforms single-stage methods and offers competitive performance compared to multistage methods, all while utilizing fewer parameters. A video is available at https://youtu.be/HNKJjQH0MPc.

Mobile-UI-Repair: a deep learning based UI smell detection technique for mobile user interface.

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Unscented particle filtering for estimation of shipboard deformation based on inertial measurement units.

Shipboard is not an absolute rigid body. Many factors could cause deformations which lead to large errors of mounted devices, especially for the navigation systems. Such errors should be estimated and compensated effectively, or they will severely reduce the navigation accuracy of the ship. In order to estimate the deformation, an unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, a nonlinear shipboard deformation model is built. Simulations demonstrated the accuracy reduction due to deformation. Then an attitude plus angular rate match mode is proposed as a frame to estimate the shipboard deformation using inertial measurement units. In this frame, for the nonlinearity of the system model, an unscented particle filter method is proposed to estimate and compensate the deformation angles. Simulations show that the proposed method gives accurate and rapid deformation estimations, which can increase navigation accuracy after compensation of deformation.

Adaptive super-twisting sliding mode altitude trajectory tracking control for reentry vehicle.

This paper addresses the altitude trajectory tracking control problem of reentry vehicle subject to bounded uncertainty. A new continuous adaptive super-twisting sliding mode control (ASTSMC) method is developed based on conventional super-twisting sliding mode control (STSMC) and adaptive gain technique, which can improve tracking accuracy and achieve high control performance. Based on adaptive gain technique, the designed ASTSMC method requires no prior information on uncertainty and avoids the overestimation of control gain, then the unexpected chattering phenomenon is alleviated. By employing fast power rate reaching law and modified fast nonsingular terminal sliding mode (FNTSM) surface, the designed controller achieves faster convergence and stronger robustness than conventional STSMC methods. Furthermore, the finite-time stability of closed-loop system is proved through Lyapunov theory. Simulation results are executed to validate the superiority of the proposed controller.

Discrete-Time Control Barrier Function: High-Order Case and Adaptive Case.

This article proposes the novel concepts of the high-order discrete-time control barrier function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to guarantee forward invariance of a safe set for discrete-time systems of high relative degree. An optimization problem is then established unifying high-order discrete-time CBFs with discrete-time control Lyapunov functions to yield a safe controller. To improve the feasibility of such optimization problems, the adaptive discrete-time CBF is designed, which can relax constraints on system control input through time-varying penalty functions. The effectiveness of the proposed methods in dealing with high relative degree constraints and improving feasibility is verified on the discrete-time system of a three-link manipulator.

EMPC with adaptive APF of obstacle avoidance and trajectory tracking for autonomous electric vehicles.

In this paper, event-triggered model predictive control (EMPC) with adaptive artificial potential field (APF) is designed to realize obstacle avoidance and trajectory tracking for autonomous electric vehicles. An adaptive APF cost function is added to achieve obstacle avoidance and guarantee stability. The optimization problem for MPC is feasible by considering a special obstacle avoidance constraint. An event-triggered mechanism is proposed to reduce computational burden and ensure effectiveness of obstacle avoidance. Input and state constraints of autonomous electric vehicles are considered in both feasibility and stability by a robust terminal set. Effectiveness of both obstacle avoidance and trajectory tracking is shown by experimental results on autonomous electric vehicles.

Switched Model Predictive Control for Nonholonomic Mobile Robots Under Adaptive Dwell Time.

In this article, switched model predictive control (MPC) is proposed for nonholonomic mobile robots with adaptive dwell time and a dual-terminal set. The dual-terminal set is used to reduce on-line complexity of the switched MPC for the nonholonomic mobile robots with multiple constraints. By a switched signal with the adaptive dwell time, cost functions are switched to improve control performance under multiple constraints. The switched MPC with feasibility and stability can adjust a tradeoff between control performance and computational complexity for the closed-loop system. Simulation results are given to illustrate superiority of the switched MPC for nonholonomic mobile robots.