Reliability-based analysis of frequency of closed-loop vibration control systems with uncertain parameters

This article proposes a fault diagnosis method for closed-loop satellite attitude control systems based on a fuzzy model and parity equation. The fault in a closed-loop system is propagated with the feedback loop, increasing the difficulty of fault diagnosis and isolation. The study uses a Takagi-Sugeno (T-S) fuzzy model and parity equation to diagnose and isolate a fault in a closed-loop satellite attitude control system. A fully decoupled parity equation is designed for the closed-loop satellite attitude control system to generate a residual that is sensitive only to a specific actuator and sensor. A T-S fuzzy model is used to describe the nonlinear closed-loop satellite attitude control system. With the combination of the T-S fuzzy model and fully decoupled parity equation, the fuzzy parity equation (FPE) of the nonlinear system can be obtained. Then this article uses a parameter estimator based on a Kalman filter to identify deviations and scale factor changes from information contained in the residuals generated by the FPE. The actuator and sensor fault detection and isolation simulation of the three-axis stable satellite attitude control system is provided for illustration.

1 - The Basics

Цель. Целью статьи является определение факторов пульсаций замкнутой системы автоматического регулирования (САР) мешающих гармоник, содержащей полупроводниковый преобразователь с двухсторонней широтноимпульсной модуляцией (ШИМ), а также подтверждение теоретических предпосылок о возможности самокомпенсации действия факторов пульсаций в системе с двухсторонней ШИМ. Методика. Для проведения работы использовались: классическая теория электрических цепей, методы гармонического анализа и теория обобщенных
\nфункций. Результаты. Получены выражения, математически связывающие факторы пульсаций, значения коэффициентов демпфирования и регулируемого параметра для разных частот мешающих гармоник в системе с двухсторонней ШИМ. Научная новизна. Экспериментально подтверждены теоретические предпосылки о самокомпенсации действия факторов пульсаций в системе с двухсторонней ШИМ; Практическое значение. Использование
\nрезультатов работы позволит создать замкнутую САР для эффективного подавления мешающих гармоник в контактной сети постоянного тока.

Transcranial focused ultrasound and electrocorticography-based closed-loop epilepsy control system for an awake rat model

Laser-based metal additive manufacturing (MAM), particularly laser powder bed fusion (LPBF) and laser directed energy deposition (L-DED), is a transformative technology enabling the production of complex, high-performance metal components with precision and minimal waste. However, its wider adoption is hindered by challenges in ensuring repeatability and consistency due to the complex, dynamic nature of the process. Closed-loop systems with in-situ monitoring and control have gained attention for quality assurance, where machine learning (ML) models are crucial for correlating sensor data with process state diagnosis and facilitating control actions. This review adopts an ML perspective to elucidate the role of ML in closed-loop control systems. It first outlines sensor data types and preprocessing methods used as ML datasets, then discusses roles and applications of ML models for in-situ monitoring, highlighting limitations and potential solutions. An ML-based closed-loop control framework is also presented, with emphasis on the integration of ML in the control component. Finally, future development directions of advanced ML approaches are envisioned. This review provides new insights into enhancing the role of ML models in laser-based MAM closed-loop control systems, serving as a crucial aid in the development of intelligent control systems.

A study was conducted to determine if continuous subcutaneous glucose monitoring (from MiniMed CGMS) could be used in real-time to control blood sugar level (BSL) in patient with critical illness. A closed-loop control system was constructed to use CGMS in a real-time manner, coupled with a proportional integral (PI) control algorithm based on the sliding scale approach, for automatic intravenous infusion of insulin to patients. A total of five subjects with high BSL (>10 mmol/L) participated in formal studies of the closed-loop control system. Subjects were recruited from critically ill patients in the intensive care unit (ICU) after informed consent was obtained. Error grid analysis showed that 64.6% of the BSL readings as determined in real time using CGMS sensor, when compared to conventional BSL reading as determined in real time using CGMS sensor, when compared to conventional BSL measurements on blood drawn from an arterial line, was clinically accurate (i.e.,<20% deviation from glucometer value). In the five patients who underwent closed-loop control, the controller managed to control only one patient's glycaemia without any manual intervention. Manual intervention was required due to the real-time sensor reading deviating more than 20% from the glucometer value, and also as a safety mechanism. Test on equality of mean and variance for BSL attained prior to, during, and post trial showed that the controller's performance was comparable to manual was comparable to manual control. We conclude that the automatic sliding scale approach of closed-loop BSL control is feasible in patients in intensive care. More work is needed in the refinement of the algorithm and the improvement of real-time sensor accuracy.

Using the interval finite-element method, the vibration control problem of structures with interval parameters is discussed, which is approximated by a deterministic one. Based on the first-order Taylor expansion, a method to solve the interval dynamic response of the closed-loop system is presented. The expressions of the interval stiffness and interval mass matrix are developed directly with the interval parameters. With matrix perturbation and interval extension theory, the algorithm for estimating the upper and lower bounds of dynamic responses is developed. The results are derived in terms of eigenvalues and left and right eigenvectors of the second-order systems. The present method is applied to a vibration system to illustrate the application. The effect of the different levels of uncertainties of interval parameters on responses is discussed. The comparison of the present method with the classical random perturbation is given, and the numerical results show that the present method is valid when the parameter uncertainties are small compared with the corresponding mean values.

According to large randomized trials, results suggest that maintaining normoglycemia postoperatively through tight glycemic control (TGC) and intensive insulin therapy (IIT) can improve surgical outcomes as well as reduce mortality and morbidity in critically ill patients. However, trials examining the effects of TGC have had conflicting results. Systematic reviews and meta-analyses have also led to differing conclusions. The main reason these clinical trials and meta-analyses show negative results for TGC is the high incidence of hypoglycemia induced by IIT. This could not be prevented because there is no reliable technique that can avoid this condition during IIT. The development of accurate, continuous blood glucose monitoring devices and closed-loop systems for computer-assisted blood glucose control in the intensive care unit (ICU) will probably help avoid hypoglycemia in these situations. The STG closed-loop glycemic control system was introduced to our department to be used and evaluated for strict serum glucose control with no hypoglycemic episodes during IIT in the surgical ICU, to reduce the workload of ICU nurses, and to decrease incidents related to the management of blood glucose levels according to manual conventional venous infusion insulin therapy. The goal of our team was to use the STG closed-loop glycemic control system for perioperative TGC in surgical patients to solve the complications of IIT and reduce risk of hypoglycemia. The challenge at our hospital demonstrated that the STG closed-loop glycemic control system can be expected to achieve TGC with no occurrence of hypoglycemia induced by IIT after surgery.

This paper studies the attitude control problem for the longitudinal motion of a hypersonic aircraft model containing 28 inertial and aerodynamic uncertain parameters given by NASA Langley Research Center. To handle the nonlinear dynamics and uncertain parameters, ADRC for the unobservable and unmatched disturbance is designed. Besides, the properties of the closed-loop system is discussed in two aspects: stability and robustness to the uncertainties. The stability of the closed-loop system is proved despite of nonlinear uncertain dynamics. Besides, this paper points out that the closed-loop control system is sensitive to five uncertain parameters and it also gives the stability regions of these five uncertain parameters. Finally, numerical simulation exercises are made to validate the theoretical analysis.

Objective. Photothermal neural stimulation has been developed in a variety of interfaces as an alternative technology that can perturb neural activity. The demonstrations of these techniques have heavily relied on open-loop stimulation or complete suppression of neural activity. To extend the controllability of photothermal neural stimulation, combining it with a closed-loop system is required. In this work, we investigated whether photothermal suppression mechanism can be used in a closed-loop system to reliably modulate neural spike rate to non-zero setpoints. Approach. To incorporate the photothermal inhibition mechanism into the neural feedback system, we combined a thermoplasmonic stimulation platform based on gold nanorods (GNRs) and near-infrared illuminations (808 nm, spot size: 2 mm or 200 μm in diameter) with a proportional-integral (PI) controller. The closed-loop feedback control system was implemented to track predetermined target spike rates of hippocampal neuronal networks cultured on GNR-coated microelectrode arrays. Main results. The closed-loop system for neural spike rate control was successfully implemented using a PI controller and the thermoplasmonic neural suppression platform. Compared to the open-loop control, the target-channel spike rates were precisely modulated to remain constant or change in a sinusoidal form in the range below baseline spike rates. The spike rate response behaviors were affected by the choice of the controller gain. We also demonstrated that the functional connectivity of a synchronized bursting network could be altered by controlling the spike rate of one of the participating channels. Significance. The thermoplasmonic feedback controller proved that it can precisely modulate neural spike rate of neural activity in vitro. This technology can be used for studying neuronal network dynamics and might provide insights in developing new neuromodulation techniques in clinical applications.

While linear covariance analysis is widely used for navigation system design and analysis, it is often overlooked as a tool for closed-loop guidance navigation and control (GN&amp;C) system design and analysis. This article presents an overview of the techniques and methods required to develop a linear covariance analysis tool for a close-loop GN&amp;C system. Then, using a simple nonlinear closed-loop GN&amp;C problem as a guide, the capabilities of linear covariance analysis for the design and analysis of closed-loop systems are demonstrated. It is shown that linear covariance can be accurately applied to a closed-loop system with time-to-go guidance, dead-reckoning navigation, and a Kalman filter for state estimation. The accuracy and efficiency of linear covariance analysis is shown by direct comparison to Monte Carlo analysis results, and the value of linear covariance analysis is highlighted by presenting several analysis capabilities that are often required in the design and analysis of closed-loop GN&amp;C systems. It is also shown how the efficiency of linear covariance enables new design methodologies, one of which is presented in this article, that would otherwise be prohibitive with Monte Carlo analysis.

Motor brain-machine interfaces (BMI) allow subjects to control external devices by modulating their neural activity. BMIs record the neural activity, use a mathematical algorithm to estimate the subject's intended movement, actuate an external device, and provide visual feedback of the generated movement to the subject. A critical component of a BMI system is the control algorithm, termed decoder. Significant progress has been made in the design of BMI decoders in recent years resulting in proficient control in non-human primates and humans. In this review article, we discuss the decoding algorithms developed in the BMI field, with particular focus on recent designs that are informed by closed-loop control ideas. A motor BMI can be modeled as a closed-loop control system, where the controller is the brain, the plant is the prosthetic, the feedback is the biofeedback, and the control command is the neural activity. Additionally, compared to other closed-loop systems, BMIs have various unique properties. Neural activity is noisy and stochastic, and often consists of a sequence of spike trains. Neural representations of movement could be non-stationary and change over time, for example as a result of learning. We review recent decoder designs that take these unique properties into account. We also discuss the opportunities that exist at the interface of control theory, statistical inference, and neuroscience to devise a control-theoretic framework for BMI design and help develop the next-generation BMI control algorithms.

We succeeded in miniaturizing a needle-type glucose monitoring system with characteristics suitable for application in a wearable, closed-loop control system. A wearable artificial endocrine pancreas (12 X 15 X 6 cm, 400 g) consisting of a sensor, a microcomputer system that calculates insulin and glucagon infusion rates, and two roller pumps was developed. Continuous glucose monitoring by a glucose sensor inserted in the subcutaneous tissue of the forearm or abdomen of healthy and diabetic volunteers revealed that glucose concentrations in subcutaneous tissue were 10% lower than, but were highly correlated with, blood glucose concentrations in the range of 49-388 mg/dl. Glycemic control was established in diabetic patients by intravenously infusing insulin in response to measured glucose concentrations on a moment-to-moment basis for a period of several days. By comparing the glycemic control obtained in each patient treated with multiple insulin injections or open-loop subcutaneous insulin infusion, the superiority of feedback control with the system was clearly demonstrated. During continuous glycemic regulation, day-to-day variations of insulin requirements were recognized in both basal insulin infusion and postprandial insulin infusion rates in response to identical meals and exercise. These data suggest the feasibility of long-term glycemic control in diabetic subjects with a wearable artificial endocrine pancreas, and indicate that to overcome changes in individual metabolic characteristics on a moment-to-moment basis, a closed-loop glycemic control system may be essential for ambulatory diabetic patients.

A digital closed-loop control system with all digital phase locked loop (ADPLL) is designed and optimized, in order to improve the bias stability and transient response of the gyroscope. The nonlinear mathematical models for the closed-loop amplitude and phase control system are established. The linearization method is applied to analyze the nonlinear models. The control parameters of the drive loop are optimized. The experimental results show that the phase deviation between the demodulation reference signal of the drive loop and the sense signal is less than 0.25° in the temperature range from-40°C to 60°C, within 0.15° of the variation. At room temperature, the overshoot amount of the gyroscope control system is 4.5% with setting time of 0.12s. The bias stability is 1.45°h. Compared to the analog control scheme, the digital control system has advantages of short setting time, small overshoot, short phase locked time, large locked range, high phase precision, etc.

Neurorehabilitation is an important part of getting better for people who have nerve illnesses or accidents. Brain-computer interfaces (BCIs) and robotic exoskeletons have shown promise in better recovery results by allowing focused therapy and making neurons more flexible. This paper gives an outline of the current study on closed-loop control systems that combine robotic exoskeletons and brain-computer interfaces (BCIs) for neurorehabilitation. It also talks about the pros and cons of these systems. Closed-loop control systems try to make two-way communication possible between the user's brain activity (measured by BCIs) and the robotic suit. This way, treatments can be changed in real time based on the user's neurological signs. This method makes it possible for more personalized and flexible therapy plans, which can help people recover their movement skills and become more useful. When BCIs are combined with artificial exoskeletons, they offer many benefits, such as exact control over movement parameters, increased involvement and motivation through immersive feedback, and the chance to induce learning by coordinating brain activity and physical output. To make these systems work better, though, problems like signal processing delay, calibration issues, and the need for user training must be fixed. Recent research has shown that closed-loop control systems can be used and might be helpful in a number of neurorehabilitation situations, such as helping people who have had a stroke or spinal cord injury get their movement skills back and helping people with neurological conditions regain their independence. In the future, researchers should work on improving these systems' algorithms and hardware, running large-scale clinical studies to show that they work, and finding new ways to improve neurorehabilitation results by combining modern technology.