Synthesis of a time-optimal control system for an extremal object

The paper presents the derivation of the synthesis method for the algorithm of the time-optimal controller for a third order dynamic system. A model with an extreme second-order transient response with delay was adopted as an object of research. The constant speed actuator is represented by an integrator. The synthesis is based on using the Pontryagin’s maximum principle and describing the dynamics of a system in the state space using canonical variables. The verification of the correctness of the result obtained by the theorem of Feldbaum A.A. on the number of switchings of the direction of movement of the regulating body during the control interval has been executed. To calculate the canonical state variables, it is proposed to use the position of the regulator, the controlled value and the derivative calculated from its values, measured on real objects.

POS0774 INFLUENZA INFECTION AS A TRIGGER FOR SYSTEMIC LUPUS ERYTHEMATOSUS FLARES RESULTING IN HOSPITALIZATION

Background:Infections have been associated with a higher risk of systemic lupus erythematosus (SLE) flares, but the impact of influenza infection on SLE flares has not been evaluated.Objectives:We evaluated the association between influenza infection and SLE flares resulting in hospitalization.Methods:SLE flares resulting in hospitalization and influenza cases were ascertained from the Korean national healthcare insurance database (2014-2018). We used a self-controlled case series design. We defined the risk interval as the first 7 days after the influenza index date and the control interval was defined as all other times during the observation period of each year. We estimated the incidence rates of SLE flares resulting in hospitalization during the risk interval and control interval and compared them using a Poisson regression model.Results:We identified 1,624 influenza infections among the 1,455 patients with SLE. Among those, there were 98 flares in 79 patients with SLE. The incidence ratio (IR) for flares during the risk interval as compared with the control interval was 25.75 (95% confidence interval 17.63 – 37.59). This significantly increased the IRs for flares during the risk interval in both women (IR 27.65) and men (IR 15.30), all age groups (IR 17.00 – 37.84), with and without immunosuppressive agent (IR 24.29 and 28.45, respectively), and with and without prior respiratory diseases (IR 21.86 and 26.82, respectively).Conclusion:We found significant association between influenza infection and SLE flares resulting in hospitalization. Influenza infection has to be considered as a risk factor for flares in all SLE patients regardless of age, sex, medications, and comorbidities.References:[1]Kwong, J. C. et al. Acute Myocardial Infarction after Laboratory-Confirmed Influenza Infection. N Engl J Med 2018:378;345-353.Table 1.Incidence ratios for SLE flares resulting in hospitalization after influenza infectionRisk intervalIncidence ratio95% CIDuring risk interval for 7 days / control interval25.7517.63 – 37.59Days 1-3 / control interval21.8114.71 – 32.35Days 4-7 / control interval7.563.69 – 15.47SLE, systemic lupus erythematosus; CI, confidence intervalDisclosure of Interests:None declared

Influenza infection as a trigger for systemic lupus erythematosus flares resulting in hospitalization

In patients with systemic lupus erythematosus (SLE), there are concerns that infections may increase the risk of flares. We evaluated the association between influenza infection and SLE flares resulting in hospitalization. SLE flares resulting in hospitalization and influenza cases were ascertained from the Korean national healthcare insurance database (2014–2018). We used a self-controlled case series design. We defined the risk interval as the first 7 days after the influenza index date and the control interval was defined as all other times during the observation period of each year. We estimated the incidence rates of SLE flares resulting in hospitalization during the risk interval and control interval and compared them using a Poisson regression model. We identified 1624 influenza infections among the 1455 patients with SLE. Among those, there were 98 flares in 79 patients with SLE. The incidence ratio (IR) for flares during the risk interval as compared with the control interval was 25.75 (95% confidence interval 17.63–37.59). This significantly increased the IRs for flares during the risk interval in both women (IR 27.65) and men (IR 15.30), all age groups (IR 17.00–37.84), with and without immunosuppressive agent (IR 24.29 and 28.45, respectively), and with and without prior respiratory diseases (IR 21.86 and 26.82, respectively). We found significant association between influenza infection and SLE flares resulting in hospitalization. Influenza infection has to be considered as a risk factor for flares in all SLE patients regardless of age, sex, medications, and comorbidities.

Analysis of the behavior of search engine optimization algorithms on transient modes of non-stationary simulation processes

The article deals with the analysis of the behavior of controlled simulation models for solving the choice of extreme values of the functional, which it determines on the basis of the average integral estimate. It is assumed that the search engine optimization algorithm is directly included in the model. Of interest is the problem of estimating the duration of the control interval, i.e. system simulation time with different parameters to select the search direction. The smaller the control interval, the lower the accuracy of the estimates of the functional and, accordingly, the lower the probability of choosing the correct search direction. However, with a general limitation on the simulation time, the search algorithm performs a larger number of steps, which increases the rate of convergence to the extreme value. Thus, the choice of the duration of the control interval raises a question. The aim of the work is to build a model of a controlled process, i.e. the process of changing the controlled parameters, to estimate the rate of convergence of the optimization algorithm depending on the duration of the control interval. The analysis of the convergence of the optimization process directly on the simulation model is practically impossible due to the nonstationary nature of all ongoing processes. In this regard, the article introduces a class of conditionally non-stationary Gaussian processes, on which the efficiency of a controlled simulation model is evaluated. It is assumed that a symmetric design is used to choose the direction, and all realizations of the nonstationary process at the current point have the same initial state. As a result of the analysis of such a model, analytical expressions were obtained for estimating the accuracy of the position of the extremum depending on the duration of the control interval. The results obtained make it possible, with a general limitation of the time for conducting experiments with a simulation model, to construct a sequential analysis plan, which improves the accuracy of solving the optimization problem.

Laboratory-confirmed influenza infection and acute myocardial infarction among United States senior Veterans

Background Previous studies established an association between laboratory-confirmed influenza infection (LCI) and hospitalization for acute myocardial infarction (AMI) but not causality. We aimed to explore the underlying mechanisms by adding biological mediators to an established study design used by earlier studies. Methods With data on biomarkers, we used a self-controlled case-series design to evaluate the effect of LCI on hospitalization for AMI among Veterans Health Administration (VHA) patients. We included senior Veterans (age 65 years and older) with LCI between 2010 through 2015. Patient-level data from VHA electronic medical records were used to capture laboratory results, hospitalizations, and baseline patient characteristics. We defined the “risk interval” as the first 7 days after specimen collection and the “control interval” as 1 year before and 1 year after the risk interval. More importantly, using mediation analysis, we examined the role of abnormal white blood cell (WBC) and platelet count in the relationship between LCI and AMI to explore the thrombogenic nature of this association, thus potential causality. Results We identified 391 hospitalizations for AMI that occurred within +/-1 year of a positive influenza test, of which 31 (31.1 admissions/week) occurred during the risk interval and 360 (3.5/per week) during the control interval, resulting in an incidence ratio (IR) for AMI admission of 8.89 (95% confidence interval [CI]: 6.16–12.84). In stratified analyses, AMI risk was significantly elevated among patients with high WBC count (IR, 12.43; 95% CI: 6.99–22.10) and high platelet count (IR, 15.89; 95% CI: 3.59–70.41). Conclusion We confirmed a significant association between LCI and AMI. The risk was elevated among those with high WBC or platelet count, suggesting a potential role for inflammation and platelet activation in the underlying mechanism.

Short-Term Response of a Bridge-Winglet Sectional Model under Active Flutter Control

Active winglets, with a manually controlled attitude angle, can take advantage of the self-excited force to suppress the flutter tendency of a bridge girder. Previous studies mostly focused on the effectiveness and robustness under long-term closed-loop control. However, the deck-winglet system’s short-term response, due to the memory effect of the aerodynamic force, is of concern. A bridge sectional model with active winglets was developed to investigate this problem. Experiments with different phase shifts between the members of the winglet pair were carried out in a wind tunnel. We found that the influence residue of an instantaneous change of the control pattern lasted about three pitching cycles, indicating that a large control interval was acceptable for practical applications. A theoretical relationship between the control effect and control phase was derived to explain the results of the open-loop control. The system responses under different control intervals were analyzed by the closed-loop control, demonstrating that a large control interval was acceptable if some time-consuming algorithms are used in a practical bridge’s flutter control operation.

Research of optimal fast-acting systems of automatic control on laboratory bench

Purpose. This paper presents a laboratory bench for research of optimal and quasi-optimal automatic control system in respect of its operation speed. Laboratory bench consists of thermal unit and software and hardware suite which includes VIPA System 200 V and HMI/SCADA logic controller and Zenon Supervisor 7.0 system. Thermal unit is described by differential equation of second order pursuing the control channel “amperage in electric heating unit power converter – air temperature inside thermal unit.” Differential equation coefficients depend on screen position and centrifugal blower rotation frequency. Methodology. Reported the methodology of synthesis and results of calculation of optimal relay hypothesis for thermal unit control through the chosen channel. Were demonstrated the results of experiment in transition of thermal unit from various initial states to final states. Was shown the possibility of implementing optimal control system in respect of its operation speed in real time scale by means of software development by including algorithms for transcendence set of simultaneous equations into it or by means of development of predicative model of thermal unit. Results. Were specified conditions for application of quasi-optimal relay control hypothesis in respect of its operation speed. Reported the methodology of synthesis and results of calculation of the length of the first control interval depending on the predetermined value of readjustment. Were demonstrated the results of modeling of quasi-optimal automatic control system in respect of its operation speed in Simulink of Matlab app. Was established functional relation of the length of the first control interval depending on the predetermined value of readjustment for implementing of quasi-optimal automatic control system in real time scale with application of basic operational units of programmed logic controller.