IFAC Journal of Systems and Control最新文献

In recent years, event-triggered control has emerged as a promising strategy for addressing resource constraints in networked control systems (NCSs), such as limited life-time of battery capacity. This paper explores the development of a periodic event-triggered controller through a model-free design, assuming unknown plant dynamics. The design of the event-triggered controller employs an emulation-based approach, which is divided into solving two sub-problems: the problem for designing parameters for the time-triggered (periodic) control law, and the problem for designing parameters for the event-triggered condition. In particular, both problems will be solved through the usage of two distinct Bayesian optimization algorithms. The first problem is addressed by adapting basic Bayesian optimization to include network utilization by considering the number of communication time steps during optimization. The second problem employs constrained Bayesian optimization to incorporate explicit performance constraints within the optimization process. A numerical example is provided to demonstrate the effectiveness of the proposed method.

The glucagon effect is understudied in type 1 diabetes (T1D) simulators, without a clear consensus on the pharmacodynamics of glucagon over glucose. Glucagon receptors dynamics could present a significant contribution to T1D simulators, making them more physiologically accurate without an excessive increase in complexity. This work analyzes the receptors model contributions to glucose dynamics using a model proposed in previous work. Then, the model is assessed from two different perspectives: (1) A clinical dataset of the influence of diet (high or low carbohydrate content) on two consecutive glucagon doses (100 and 500 $\mu$g) is used to identify the model parameters and (2) three other glucagon action models from the literature are also identified to serve as comparators. Different identification methods are used to adapt to the distinctive features of the dataset. The root mean square error (RMSE) and the Akaike Information Criterion (AIC) were the discerning metrics used to compare the models fittings. Results show that the receptors model offers the lowest RMSE and AIC in contrast to the comparators. This model will hence be helpful in the development of accurate T1D simulators.

Estimation of the breathing effort and relevant lung parameters of a ventilated patient is essential to keep track of a patient’s clinical condition. The aim of this paper is to increase estimation accuracy through experiment design. The main method is an experiment design approach across multiple breaths within a linear regression framework to accurately identify the patient’s condition. Identifiability and persistence of excitation are used to formulate an estimation problem with a unique solution. Furthermore, Fisher information is used for assessing the parameters sensitivity to slight changes of the ventilator settings to improve the variance of the estimation. The estimation method is applied to simulated patients who breathe regularly but also to patients who have variable breathing patterns. A virtual experiment is conducted for both situations to generate estimation results. The results are analyzed using mathematical tools and show that uniquely estimating the lung parameters and breathing effort over multiple breaths for both regularly and variably breathing patients is possible in the presented framework. The proposed estimation method obtains clinically relevant estimates for a large set of breathing disturbances from the simulation case-study.

The use of near-Earth space is currently complicated by presence of space debris objects in Earth’s orbit, which include spent stages of launch vehicles, inoperative satellites, and other large and small artificial objects. One approach to solving the problem of space debris involves docking and capturing a non-cooperative space object or spacecraft (target) with a so-called service spacecraft (chaser) for further actions to repair, refuel or change its orbit. Rendezvous and docking are complicated by the rotation of uncontrolled space objects caused by various factors. To perform this task, it is necessary to know the parameters of the orbital, rotational and relative motion of the target. The parameters of the orbital motion of such objects are usually known quite accurately from measurements from the Earth. This paper examines the case of a tumbling non-cooperative target located in an elliptical orbit. It is assumed that the target relative position and orientation are measured by the computer vision system (CVS) of the chaser. In this case, the position and orientation of the graphical reference frame (GRF) associated with the known 3-D graphical model of the target are determined relative to the reference frame associated with the chaser. The specific type of CVS is not considered. It is assumed that the chaser can carry out some maneuvers near the target and all parameters of the chaser angular motion are known. Thus, the attitude of the GRF relative to inertial reference frame (IRF) can be determined. The measured parameters are not enough to ensure safe rendezvous and docking with the target. To complete this task, it is necessary to determine all kinematic and dynamic parameters of the relative motion between the spacecraft. The rest of the required parameters are estimated. Orientation and rotation parameterization is done using quaternions. The angular motion equation of the target is considered in the GRF. This makes the angular velocity estimation faster and the inertia tensor estimation more stable. Stochastic characteristics of measurement errors are considered to be unknown and are not used. The only information about the errors is the bounds of their values. To determine the relative motion parameters, we use a new dynamic set-membership filter with ellipsoidal estimates. The filter can be successfully implemented on low-power on-board processors. The properties of the proposed algorithm are demonstrated using numerical simulation. The results obtained are expected to be used in the development of a navigation system for the rendezvous and docking, developed by a group of Ukrainian space industry enterprises under the leadership of the LLC “Kurs-orbital” (https://kursorbital.com/).

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs internal conductivity distributions of a body from electrical measurements taken on its boundary. This study contributes to the field by focusing on the technological intricacies of absolute EIT imaging, which is challenged by limitations such as the resolution capacity of the hardware and the complexities introduced by imaging capacitive bodies. The novel EIT system architecture proposed enhances the accuracy of measurement by integrating current sources and Analog-to-Digital Converters (ADCs) closer to the electrodes, employing alternating current excitations to accurately capture phase information. This system uses a dynamic arrangement of surface electrodes that continuously alter their roles between current injection and voltage measurement, in a synchronized sequence, to ensure the accuracy of the measurements. The paper describes the design and implementation of both the excitation and measurement subsystems, highlighting the use of digital signal demodulation near the electrode to reduce data transfer issues. Experimental results confirm the system’s capability for real-time image reconstruction at 50 frames per second with precision in phase delay measurements, suggesting significant potential for clinical and industrial applications. Future work will aim to further refine signal generation with higher-speed DACs and expand to image reconstruction with more channels.

Respiratory Oscillometry is a promising technique to provide information to medical practitioners on the respiratory system of a patient in a non-invasive fashion. It focuses on identifying the respiratory impedance between two signals: the air pressure and flow at the mouth opening. However, for conscious patients or lightly sedated ventilated patients, their respiratory effort such as breathing acts as a disturbance to the parameter estimation procedure. This paper is an extension to previous research published at the IFAC 2023 World Congress (Marchal et al., 2023) that proposed a method to estimate and remove this breathing disturbance using Gaussian Process Regression in the frequency domain. In this extension, Monte Carlo simulations are performed to validate the approach and to compare it to the Local Polynomial Method for breathing patients. In addition, measurements carried out on a lung emulator in a pressure-support ventilation mode provide further evidence of the method’s effectiveness at dealing with the disturbance experienced for ventilated patients. This is a step towards treating both breathing and ventilated patients using the same technique.

In this article, we examine a Networked Control System (NCS) in which the plant and the controller communicate over a network subject to a certain communication constraint. The plant is described by discrete-time nonlinear dynamics subject to bounded disturbances. Due to an overloaded communication network, we assume that the control signal and the information from the plant (the measured output signal) cannot be transmitted simultaneously and are subject to a multiplexing constraint. The goal is to design a switching strategy that allows us to sequentially communicate given these constraints while optimizing a quadratic cost over a finite horizon. Consequently, we proceed by emulation and assume that a controller that satisfies performance requirements is already provided. The resulting optimization problem is observed to be an integer programming problem that is generally NP-complete, i.e., the complexity is exponential in the time horizon considered. To overcome this issue, we provide a different perspective on this problem than what has been presented by the community before. Our main contribution is to reformulate the problem with all its constraints to a form that renders it amenable to apply the discrete-time Pontryagin Maximum Principle to get the necessary conditions for the optimality of the control action sequence. These necessary conditions are then solved numerically by a multiple-shooting method. To validate the approach, we present some illustrative numerical experiments on an inverted pendulum. Different setups are considered and numerically analyzed: usage of a predictor when the output is not transmitted and usage of the previous value of the output when the new value is not transmitted, with or without the choice of non-transmission.

In this paper, we consider a novel mathematical modeling framework for the spread of two competitive diseases in a well-mixed population. The proposed framework, which we term a bivirus SIRIS model, encapsulates key real-world features of natural immunity, accounting for different levels of (partial and waning) virus-specific and cross protection acquired after recovery. Formally, the proposed framework consists of a system of coupled nonlinear ordinary differential equations that builds on a classical bivirus susceptible–infected–susceptible model by means of the addition of further states to account for (temporarily) protected individuals. Through the analysis of the proposed framework and of two specializations, we offer analytical insight into how natural immunity can shape a wide range of complex emergent behaviors, including eradication of both diseases, survival of the fittest one, or even steady-state co-existence of the two diseases.

We propose a method to design reduced-order output-feedback controllers for fluid flows with the use of data produced by full-order controllers. First, the full-order controller is obtained by combining an ensemble Kalman filter (EnKF) and a model predictive controller (MPC) that are designed based on the Navier–Stokes equations. The full-order controller has high computational complexity and, therefore, is not suitable for real-time implementation. Hence, we use the full-order controller in offline numerical simulations to generate data for data-driven design of the reduced-order controller with low computational complexity. We find a reduced-order subspace of a closed-loop system under the full-order control from the data. This subspace underlies the reduced-order output-feedback controller. The reduced-order state-feedback law is obtained by approximating the full-order MPC with the use of its input/output data. The reduced-order observer is designed for a reduced-order model that is derived by using the Gaussian process regression (GPR). The GPR enables us to design the reduced-order observer which can evaluate uncertainty due to state-dependent residuals of the reduced-order model. We demonstrate the proposed method for a control problem of a flow around a cylinder at the Reynolds number 100. Numerical simulations reveal that the reduced-order controller performs as almost well as the full-order controller for a set of initial states. In addition, robustness of the reduced-order controller to a temporal disturbance that is not considered in the control design is confirmed in the simulations.