Fault diagnosis method for closed-loop satellite attitude control systems based on a fuzzy parity equation

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

According to principle of model reference adaptation, a model reference fault diagnosis approach for on-orbit satellite attitude control system was proposed based on measurement and calculation of environmental torques acted on satellite. With satellite kinematics and attitude dynamics, a linear model for satellite attitude control system is established. On the basis of system parameter and structural characteristics, satellite attitude control system reference model modified by measurement and calculation of environmental torques is built. With fault residual vector generated by comparing output of reference model with real system, sensor fault detection and isolation is accomplished. Extended state observer is designed to estimate system disturbance using augmented matrix method. According to disturbance estimation and environment torque calculation, actuator fault residual vector is obtained to detect and isolate actuator fault. The simulation experiment result demonstrates that the proposed approach is effective and efficient for real-time fault diagnosis of on-orbit satellite attitude control system.

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.

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.

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

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.

Simultaneous state and actuator fault estimation for satellite attitude control systems

This paper addresses the Fault Detection and Isolation (FDI) of the problem of multiple non-concurrent faults on actuator and sensor of quadrotors, which is described by a nonlinear model. To the best of authors' knowledge, this paper provides, for the first time, a solution to the FDI problem for quadrotors affected by both actuator and sensor faults. The literature proposes FDI schemes dedicated only to actuators or only to sensors, indeed. The Non Linear Geometric Approach is based on the mathematical model of the system and allows to determine detection filters sensitive to a selected set of fault and structurally decoupled from the other faults (out of the selected set) thus providing the base for the fault isolation. Several simulation trials have been performed and the analysis of the results proves that the developed diagnostic system represents an effective solution to the problem of multiple actuator and sensor fault detection and isolation for quadrotors.

Model-based fault detection and diagnosis is an interesting method, due to economical and safety related matters. However, in practice it is very difficult to achieve accurate models for complex nonlinear plants. If the plant structure is not precisely known, the diagnosis has to be based primarily on data or heuristic information. The inherent characteristics of fuzzy logic theory make it suitable for fault detection and isolation (FDI). In this paper is proposed a modification to the regularity criterion algorithm, aiming the reduction of computational time without loss of accuracy. The fuzzy models obtained using the modified regularity criterion algorithms are optimized by a real-coded genetic algorithm. An industrial valve simulator is used to obtain several abrupt and incipient faults in the system. These faults are some of the possible faults in the real system. The fuzzy models used in the FDI system were able to detect and isolate the simulated abrupt and incipient faults

The reliability-based analysis of frequency of the vibration control problem of structures with random parameters is discussed. Based on the theory of perturbation method, closed-loop vibration control systems, reliability analysis, and PNET method, the method of reliability-based analysis of eigenvalues of closed-loop vibration control systems with uncertain parameters is studied. And the distribution function of the random eigenvalues will not be computed other than their means and variances. The standard deviations of eigenvalues of the uncertain closed-loop systems can be used to estimate the reliability of frequency. The numerical results show that the present method is effective.

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.

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&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&C system. Then, using a simple nonlinear closed-loop GN&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&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.

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.