Strict Stability Research Articles

Event triggering fixed time secondary control of DC microgrid considering FDI attacks

SummaryAiming at the problem that distributed secondary control is vulnerable to FDI attacks in DC microgrid, which affects the stability of the system, and the problems of communication redundancy and energy loss in traditional distributed secondary control, an event triggered fixed time distributed secondary control scheme based on high‐order differentiator is proposed. The high‐order differentiator is used to estimate the FDI attack signal of the system. Then, using the estimated attack signal, a fixed time distributed secondary controller based on event triggering is designed to eliminate the impact of the attack signal, and realize the adjustment of bus voltage and accurate power distribution. The strict stability and the absence of Zeno behavior are proved. The simulation results show that the proposed control strategy can significantly reduce the impact of attacks on the system, and the event triggered fixed time control reduces the energy consumption of the system and the update numbers of the controller, making the system have better dynamic performance.

WITHDRAWN: Robust voltage regulation of DC-DC buck converter with ZIP load via an energy shaping control approach

ZIP loads (the parallel combination of constant impedance loads, constant current loads and constant power loads) exist widely in power system. In order to stabilize buck converter based DC distributed system with ZIP load, an adaptive energy shaping controller (AESC) is devised in this paper. Firstly, based on the assumption that the lumped disturbances are known, a full information controller is designed in the framework of the port Hamiltonian system via energy shaping technique. An obvious merit is that it is PI-type controller, which is easy to implement in practical application. Besides, using mathematical deductive method, an estimation of the domain of attraction is given to ensure the strict stability. Furthermore, to eliminate the influence of parameter perturbations on the system, a disturbance observer is proposed to reconstruct the lumped disturbances and then the estimated terms are introduced to above controller to form an adaptive control scheme. In addition, the strict stability analysis of the closed-loop system is given. Lastly, the simulation and experiment results are presented for assessing the designed controller.

Navigating legal pathways for accelerating urban energy transition: A comprehensive deep analysis of photovoltaic power prediction and policy instruments

This research proposes a navigation method that explores how to accelerate urban energy transformation through legal means from the perspective of energy law, in order to improve the accuracy of photovoltaic output power prediction in sustainable energy transformation. Firstly, a deep learning model based on adversarial autoencoder (AAE) architecture was introduced to predict photovoltaic output power according to clean environment policies. Secondly, in order to effectively fine tune the parameters of AAE, a heuristic optimization method based on the Imperial Competition Algorithm was proposed to optimize the AAE configuration to adapt to the unique characteristics of photovoltaic systems, thereby improving prediction accuracy. In addition, the study analyzed the challenges and opportunities faced by current urban energy transformation, and then proposed sustainable ways to accelerate urban energy transformation from the aspects of formulating transformation plans, promoting technological innovation, promoting green transportation, promoting building energy conservation, and developing circular economy. The synergistic effect of deep learning and heuristic optimization, combined with strict stability and sensitivity assessment, puts this research at the forefront of advancing photovoltaic output power prediction methods, which is of great significance for optimizing renewable energy utilization and grid integration.

Estimator-based neural adaptive event-triggered control for strict-feedback nonlinear systems with incomplete measurements

This article addresses a neural adaptive event-triggered tracking control for a class of strict-feedback nonlinear dynamics with incomplete measurements, which is novel on the research of Cyber-physical Systems. The incomplete measurement problem caused by packet loss, saturation, and other issues during data transmission can lead to the unavailability of system state variables, which can degrade system performance and even lead to instability. To solve these problems, a state estimator for data-losing case and two controllers for normal and data-losing cases are designed utilising event-triggered strategies which can reduce the burden of calculation and data transmission. Radial basis function neural networks are adopted to approximate the unknown nonlinear system functions. A strict stability analysis in probability shows that the control laws for the considered strict-feedback nonlinear system can guarantee all the closed-loop to be uniformly ultimately bounded in mean square. Two examples are performed to demonstrate the effectiveness of the provided control method.

Practical Finite-Time Event-Triggered Control of Underactuated Surface Vessels in Presence of False Data Injection Attacks

Abstract The results of studies on a trajectory-tracking problem affected by false data injection attacks (FDIAs) and internal and external uncertainties are presented in this paper. In view of the FDIAs experienced by the system, we compensate for the serious navigation deviation caused by malicious attacks by designing an online approximator. Next, we study the internal and external uncertainties introduced by environmental factors, system parameter fluctuations, or sensor errors, and we design adaptive laws for these uncertainties to approximate their upper bounds. To further enhance the response velocity and stability of the system, we introduce finite-time technology to ensure that the unmanned underactuated surface vessels (USVs) reach the predetermined trajectory-tracking target within finite time. To further reduce the update frequency of the controller, we introduced event-triggered control (ETC) technology. This saves the system’s communication resources and optimizes the system. Through Lyapunov stability theory, a strict and complete stability analysis is provided for the control scheme. Finally, the effectiveness of the control scheme is verified using two sets of simulations.

Generic Stability Implication From Full Information Estimation to Moving-Horizon Estimation

Optimization-based state estimation is useful for handling of constrained linear or nonlinear dynamical systems. It has an ideal form, known as full information estimation (FIE) which uses all past measurements to perform state estimation, and also a practical counterpart, known as moving-horizon estimation (MHE) which uses most recent measurements of a limited length to perform the estimation. This work reveals a generic link from robust stability of FIE to that of MHE, showing that the former implies at least a weaker robust stability of MHE which implements a long enough horizon. The implication strengthens to strict robust stability of MHE if the corresponding FIE satisfies a mild Lipschitz continuity condition. The revealed implications are then applied to derive new sufficient conditions for robust stability of MHE, which further reveals an intrinsic relation between the existence of a robustly stable FIE/MHE and the system being incrementally input/output-to-state stable.

1213. Application of Short Course Criteria for Community-Acquired Pneumonia (CAP) Among Patients Admitted Within a 17-community Hospital Telehealth Antimicrobial Stewardship Program (Tele-ASP)

Abstract Background A recent randomized controlled trial by Dinh et al. found three days of antibiotics was appropriate for certain community-acquired pneumonia (CAP) patients, but the real-world applicability of this study remains to be evaluated. In February 2022, a telehealth antimicrobial stewardship program (Tele-ASP) intervention was implemented across 17 small community hospitals to identify patients meeting clinical stability criteria used by Dinh et al. for short course therapy (3 days). An alert was implemented to notify the infectious diseases telehealth (Tele-ID) pharmacist of patients on room air for whom an antibiotic was ordered for pneumonia. Methods This IRB-approved study aimed to evaluate feasibility and impact of the intervention. Patients with severe or complicated CAP, known immunosuppression, or who were transferred were excluded. The primary outcome was total antibiotic duration. Secondary outcomes included whether the total duration was greater than 5 and 7 days and total IV antibiotic duration. Continuous antibiotic duration endpoints were assessed using Mann Whitney U and Fisher's Exact tests were used for categorical endpoints. Results Of 480 patients screened, only 148 (31%) patients met early clinical stability criteria. The primary reason for not meeting criteria was requirement of supplemental oxygen on Day 3 or greater on antibiotics (n=145). This proportion is far lower than previous studies, including that by Dinh et al., which found 60-70% patients met stability criteria. Of the 90 patients included, 27 patients had a Tele-ID review. Patients with an intervention were less likely to receive > 5 days of antibiotics (59% vs 73%) and > 7 days of antibiotics (19% vs. 32%), although these differences were not statistically significant. Conclusion This study relied on accuracy of oxygen charting, which may partly explain relatively lower proportion of patients meeting strict clinical stability criteria and likely limits feasibility of a scalable intervention. A pragmatically modified approach for this Tele-ASP intervention (i.e. allowance for no more than 1 unmet criterion) is an important area for further exploration and could improve the feasibility of a scalable telehealth intervention. Disclosures All Authors: No reported disclosures

Quadrotor Position and Attitude Tracking Using Advanced Second-Order Sliding Mode Control for Disturbance

Although sliding mode control (SMC) provides powerful control performance, it exhibits chattering phenomena that can lead to operational issues in quadrotor systems when certain thresholds are reached. To address this limitation, this study introduces second-order sliding mode control (SOSMC), which significantly reduces chattering. Furthermore, a modified version of the SOSMC, called advanced second-order sliding mode control (ASOSMC), is proposed by incorporating an additional term to enhance its control capabilities. The ASOSMC exhibits a stability higher than that of traditional SOSMC during quadrotor flight. We established the stability of the ASOSMC system using a more powerful strict Lyapunov stability analysis instead of the conventional Lyapunov stability employed in SMC. This paper presents the design and performance evaluation of the ASOSMC via simulation and compares it with those of conventional SMC (CSMC) and SOSMC. The simulation results confirm that the ASOSMC offers superior control performance in quadrotor systems.

Stability of Carbon Supported Silver Electrocatalysts for Alkaline Oxygen Reduction and Evolution Reactions.

Ag-based electrocatalysts are promising candidates to catalyze the sluggish oxygen reduction reaction (ORR) in anion exchange membrane fuel cells (AEMFC) and oxygen evolution reaction (OER) in unitized regenerative fuel cells. However, to be competitive with existing technologies, the AEMFC with Ag electrocatalyst must demonstrate superior performance and long-term durability. The latter implies that the catalyst must be stable, withstanding harsh oxidizing conditions. Moreover, since Ag is typically supported by carbon, the strict stability requirements extend to the whole Ag/C catalyst. In this work, Ag supported on Vulcan carbon (Ag/VC) and mesoporous carbon (Ag/MC) materials is synthesized, and their electrochemical stability is studied using a family of complementary techniques. We first employ an online scanning flow cell combined with inductively coupled plasma mass spectrometry (SFC-ICP-MS) to estimate the kinetic dissolution stability window of Ag. Strong correlations between voltammetric features and the dissolution processes are discovered. Very high silver dissolution during the OER renders this material impractical for regenerative fuel cell applications. To address Ag stability during AEMFC load cycles, accelerated stress tests (ASTs) in O2-saturated solutions are carried out in rotating disk electrode (RDE) and rotating ring-disk electrode (RRDE) setups. Besides tracking the ORR performance evolution, an ex situ long-term Ag dissolution study is performed. Moreover, morphological changes in the catalyst/support are tracked by identical-location transmission electron microscopy (RDE-IL-TEM). Voltammetry analysis before and after AST reveals a smaller change in ORR activity for Ag/MC, confirming its higher stability. RRDE results reveal a higher increase in the H2O2 yield for Ag/VC after the ASTs. The RDE-IL-TEM measurements demonstrate different degradation processes that can explain the changes in the long term performance. The results in this work point out that the stability of carbon-supported Ag catalysts depends strongly on the morphology of the Ag nanoparticles, which, in turn, can be tuned depending on the chosen carbon support and synthesis method.

On Absolute Stability of Open-Loop Synchronized Single-Phase Grid-Following Inverters

This article is concerned with the strict stability problem for grid-following inverters subject to synchronous nonlinearity and large transient disturbances. Firstly, considering that the complex nonlinear structure of the conventional phase-locked loop (PLL)-based grid synchronization scheme may difficult to match the existing intuitive nonlinear analysis methods, a simplified open-loop grid synchronizer is developed for the grid-following control. Subsequently, the sector-bounded condition of the system nonlinear factor is derived by setting the grid voltage and power control variation intervals, and the frequency-domain conditions for absolutely stable are further obtained with the help of the circle criterion. The derived conditions illustrate that the stability assessment is intuitive, because it is only necessary to observe the graphical position of the disk <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d(-\psi,-\phi)$</tex-math></inline-formula> with respect to the Nyquist curve of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-\Omega _{\alpha }(s)$</tex-math></inline-formula> . Finally, the effects of weak grid and power control variations on system stability are further analyzed, and the effectiveness of the proposed theories is fully supported by transient experimental results.

A novel sequential-based hybrid approach incorporating physical modeling and deep learning for multiphase subsurface flow simulation

Multiphase subsurface flows are of great importance for both the sustainable utilization of resources and ecological protection. The simulation of multiphase flow is conventionally resolved using either physics-driven numerical discretization or data-driven machine learning strategy. Despite being perceived as distinct paradigms, their algorithmic-level synergy is expected to bring tremendous advantages. Within the implicit pressure and explicit saturation scheme, this study constructs deep learning model for pressure approximation using an ensemble of equal-probable permeability realizations and the corresponding pressure/velocity fields under randomized injection/production strategies. Specifically, this study proposes two network models, namely U-Net and Fourier Neural Operator, and empower them by physics-guided loss functions, which not only improve the training efficiency but also ensure the physical continuity of the velocity field and maintain the overall mass balance of the coupled system. Moreover, we advance the saturation front using a standard explicit solver under its strict stability condition. The results demonstrate that our hybrid approach performs strong adaptability and flexibility under different permeability and injection/production conditions enclosed in the model training set. For all test cases, we achieve less than 0.6% relative errors for both pressure and saturation solutions. More importantly, our well-trained hybrid solver only uses a fraction of second, which is several orders of magnitude faster than the traditional physics-driven numerical solvers. Our newly designed solution architecture provides a physically reliable technique for superfast multiphase subsurface flow field approximation, which is believed to be of great value for the challenging production optimization and uncertainty quantifications of subsurface resource utilizations.

Neuroadaptive Control for Complicated Underactuated Systems With Simultaneous Output and Velocity Constraints Exerted on Both Actuated and Unactuated States.

Due to limited workspace and safety requirements for practical underactuated mechanical systems, it is necessary to restrict all to-be-controlled variables and their velocities within preset ranges, avoid collisions/overshoots, and improve braking performance. However, due to fewer available control inputs, it is quite challenging to ensure error elimination and full-state constraints for both actuated/unactuated variables, including displacements/angles and their derivatives (i.e., velocity signals) together. To handle the above issues, this article designs a new adaptive full-state constraint controller for a class of uncertain multi-input-multi-output (MIMO) underactuated systems. First, different output constraint-related auxiliary functions are constructed in the Lyapunov function candidate to generate nonlinear displacement-/angle-limited terms to control all state variables. Then, this article handles velocity constraints in a new manner, where the elaborately designed velocity constraint-related terms are directly introduced into the presented controller (instead of the Lyapunov function candidate), and strict theoretical analysis is provided by utilizing reduction to absurdity. Hence, both actuated and unactuated velocity constraints are ensured to further improve transient performance. In addition, the impact of model uncertainties is addressed online to realize accurate positioning control for all state variables. Compared with current studies of underactuated systems, this article presents the first adaptive controller to address output and velocity constraints for actuated and unactuated variables together; moreover, their asymptotic convergence is proven by strict stability analysis, which is important both theoretically and practically. In the end, the feasibility and robustness of the proposed controller are verified by hardware experiments.

Universal Absolute Stability Criterion for Single-Phase Grid-Following Inverters Equipped With Orthogonal-Normalization-Based Grid Synchronizer

The stability problem of grid-following inverters has been widely investigated. Nevertheless, existing literature has not yet proposed a strict stability analysis method for the overall grid-following inverter system with nonlinear nature. Focusing on single-phase systems, by using frequency-domain nonlinear theory, this article proposes a universal absolute stability criterion for the single-phase grid-following inverters (SGFIs) equipped with orthogonal-normalization-based grid synchronizer. It will be shown that a SGFI will be absolutely stable under small- and large-disturbances if both the traditional impedance criterion and the synchronous nonlinear criterion are satisfied. The usefulness and applicability of the proposed new criterion is demonstrated by a case study of the conventional LCL-type SGFI system. Finally, the theoretical analysis is fully supported by a series of steady-state and transient experimental tests.

Generalized Criteria for Admissibility of Singular Fractional Order Systems

Unified frameworks for fractional order systems with fractional order 0&lt;α&lt;2 are worth investigating. The aim of this paper is to provide a unified framework for stability and admissibility for fractional order systems and singular fractional order systems with 0&lt;α&lt;2, respectively. By virtue of the LMI region and GLMI region, five stability theorems are presented. Two admissibility theorems for singular fractional order systems are extended from Theorem 5, and, in particular, a strict LMI stability criterion involving the least real decision variables without equality constraint by isomorphic mapping and congruent transform. The equivalence between the admissibility Theorems 6 and 7 is derived. The proposed framework contains some other existing results in the case of 1≤α&lt;2 or 0&lt;α&lt;1. Compared with published unified frameworks, the proposed framework is truly unified and does not require additional conditional assignment. Finally, without loss of generality, a unified control law is designed to make the singular feedback system admissible based on the criterion in a strict LMI framework and demonstrated by two numerical examples.

Patching Locking Bugs Statically with Crayons

The Linux Kernel is a world-class operating system controlling most of our computing infrastructure: mobile devices, Internet routers and services, and most of the supercomputers. Linux is also an example of low-level software with no comprehensive regression test suite (for good reasons). The kernel’s tremendous societal importance imposes strict stability and correctness requirements. These properties make Linux a challenging and relevant target for static automated program repair (APR). Over the past decade, a significant progress has been made in dynamic APR. However, dynamic APR techniques do not translate naturally to systems without tests. We present a static APR technique addressing sequential locking API misuse bugs in the Linux Kernel. We attack the key challenge of static APR, namely, the lack of detailed program specification, by combining static analysis with machine learning to complement the information presented by the static analyzer. In experiments on historical real-world bugs in the kernel, we were able to automatically re-produce or propose equivalent patches in 85% of the human-made patches, and automatically rank them among the top three candidates for 64% of the cases and among the top five for 74%.

String stability of automated vehicles based on experimental analysis of feedback delay and parasitic lag

Recent literature shows that automated vehicle (AV) significantly impacts traffic flow stability. However, as a widely used longitudinal AV technology, the Adaptive Cruise Control (ACC) system still has several unknown features that lack field data for modeling. We ran a series of novel tests with two ACC vehicles to simulate the behavior of the AV platoon in the field experiment. We designed a linear car-following controller in the upper layer system and tested the string stability of the ACC vehicle platoon. Data is collected through the vehicle's GPS sensor and upper controller. These data can help us estimate the feedback delay in the upper layer and parasitic lag in the lower layer. The data analysis results specify the range of different time delays and further optimize the dynamic response model in the lower layer. Then we propose an improved longitudinal model based on a first-order inertia system with a pure time delay in the dynamic response process. The experimental results show that the improved longitudinal model is twenty percent more accurate than the traditional first-order inertia model. Finally, we analyze the string stability of the improved longitudinal model. The results indicate that the improved longitudinal model has more strict stability conditions than the traditional model. Although the current feedback delay can satisfy the string stability criterion, the parasitic lag in the dynamics response process cannot satisfy the stability criterion. In this study, a customized ACC car-following controller is implemented in the field experiments, and thus all hypotheses and analysis results are validated by field data.

Multilayer neuroadaptive force control of electro-hydraulic load simulators with uncertainty rejection

In this paper, an innovative multilayer neuroadaptive controller is firstly proposed to further improve the output force tracking performance of electro-hydraulic load simulators with uncertainty rejection. Significantly, a highly nonlinear load simulator driven by the single-rod electro-hydraulic actuators is considered, which has universal research significance in relation to the relevant studies. Moreover, smooth endogenous uncertainties, non-smooth endogenous uncertainties in the load pressure dynamics, and exogenous disturbances can all be effectively compensated in a feedforward way, which makes it possess higher load simulation accuracy and wider applicability to working conditions. In detail, multilayer neural networks and extended state observer are employed to estimate endogenous uncertainties and exogenous disturbances, respectively. Specially, the neural network weights are on-line updated via the composite errors comprised of the force tracking and estimate errors, which can increase the resulting learning ability. The closed-loop stability is guaranteed via the strict Lyapunov stability analysis and extensive comparative application results are achieved to validate the high-performance efficacy of the proposed intelligent learning algorithm.

Dual-Loop Dynamic Control of Cable-Driven Parallel Robots Without Online Tension Distribution

Achieving high-precision position control while maintaining positive cable tensions is the most challenging issue for the motion control of cable-driven parallel robots, which should be considered significantly. Different from the existing control schemes with online tension distribution that needs real-time computing in each control cycle, a novel dual-loop dynamic control scheme is proposed in this article, where a paralleled dual-loop tracking strategy is introduced to provide a more compatible scheme, which consists of two tracking loops: 1) the tension control loop and 2) the position control loop. In the former loop, the offline tension distribution is adopted to avoid cable hanging loosely and the real-time feasibility of the distribution method is no longer a necessary demand. In the latter loop, due to the complex dynamics characterized by the cable-driven form, the cooperative motion relation among multiple cables and inevitable external disturbances are investigated comprehensively, and the robust synchronization method is included to guarantee the high-precision position control. Afterward, the Lyapunov method is adopted to analyze the strict stability of the whole closed-loop system with both the position and tension control feedback. The experiments indicate that by synthesizing the two control loops, the proposed scheme can dramatically reduce the tracking errors in the trajectory tracking while avoiding the cable relaxation, and particularly, has a satisfactory control effect when the velocity and acceleration of the trajectory have significant oscillations. Additionally, the strong disturbance rejection ability is also validated via robustness experiments.

Locked fronts in a discrete time discrete space population model.

A model of population growth and dispersal is considered where the spatial habitat is a lattice and reproduction occurs generationally. The resulting discrete dynamical system exhibits velocity locking, where rational speed invasion fronts are observed to persist as parameters are varied. In this article, we construct locked fronts for a particular piecewise linear reproduction function. These fronts are shown to be linear combinations of exponentially decaying solutions to the linear system near the unstable state. Based upon these front solutions, we then derive expressions for the boundary of locking regions in parameter space. We obtain leading order expansions for the locking regions in the limit as the migration parameter tends to zero. Strict spectral stability in exponentially weighted spaces is also established.

Stability-certified reinforcement learning control via spectral normalization

In this study, two types of methods from different perspectives based on spectral normalization (SN) are described for ensuring the stability of a feedback system controlled by a neural network (NN). The first one is that the L2 gain of the feedback system is bounded less than 1 to satisfy a stability condition derived from the small-gain theorem. When explicitly including the stability condition, the first type of method may provide an insufficient performance on the NN controller due to its strict stability condition. To overcome this difficulty, the second type of method is proposed, ensuring local stability with a larger region of attraction. In this second type, the stability is ensured by solving linear matrix inequalities after training the NN controller. SN improves the feasibility of the a posteriori stability test by constructing tighter local sectors. Numerical experiments show that the second type of method provides sufficient performance compared with the first one and ensures sufficient stability compared with existing reinforcement learning algorithms.11Project page: https://sites.google.com/g.ecc.u-tokyo.ac.jp/stability-certified-rl-via-sn.