Stability Guarantees Research Articles

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state stability (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.

Stability prediction method of time-varying real-time hybrid testing system on vehicle-bridge coupled system

In recent years, real-time hybrid testing (RTHT) has been applied for the dynamic testing of high-speed trains running on bridges. A guarantee of stability for the RTHT system is essential to achieve a safe and reliable result. However, the inherent time-varying characteristics of the vehicle-bridge coupled system pose challenges to RTHT stability prediction. This study aims to develop a stability prediction method specifically tailored for time-varying RTHT system. Firstly, the vehicle-bridge coupled RTHT was modelled using a discrete state–space representation with a comprehensive consideration of the time-varying vehicle-bridge interaction and the dynamics of the shaking table. Subsequently, a time-varying stability criterion was derived from the periodic time-varying state matrix, forming the basis for a relative stability prediction method. The validity of the proposed method was confirmed through simulations and experiments employing a single-axle interaction within the vehicle-bridge coupled RTHT system. The coupled system consisted of a quarter-car model and simply supported beams were used as an example to evaluate the stability and accuracy of the time-varying RTHT. The results showed that the stability increased with increasing vehicle speed. Reducing the pure time delay of the shaking table improved both stability and accuracy. Increasing the effective frequency of the shaking table improved the accuracy but may reduce the stability of the RTHT.

Model Predictive Control With Stability Guarantee for Second-Order DC/DC Converters

This paper proposes a continuous-control-set model-predictive control (CCS-MPC) designed explicitly for second-order dc/dc converters such as the boost, buck, buck-boost, and non-inverting buck-boost converters. These converters share a bilinear dynamic structure that allows designing a generalized control. In contradistinction to the conventional finite-control-set approach, the proposed control uses a pulse-width modulation that allows a continuous control set with constant switching frequency. The modulation index's saturation is considered an inequality constraint in the optimization model. The proposed control guarantees stability in the sense of Lyapunov. This property is proven by using the passive structure of these converters. Experimental results on the four converters mentioned above show the superior performance of the proposed control in terms of robustness, offset, computation time in the central processing unit and, of course, stability.

A synergistic study of policy and literature on rumor governance

Rumor governance is an important guarantee for social stability and public safety. Based on the life cycle and crisis cycle model, this paper conducts a synergistic analysis of China's rumor governance policies and regulations and the core scientific research literature on rumor governance in WOS and CNKI. In this paper, we use the TF-IDF algorithm to count the word frequencies of 326 policy and regulation texts, the Jieba-RoBERTa-Kmeans model to cluster high-frequency keywords, and CiteSpace software and the LLR clustering algorithm are utilized to extract and cluster keywords from 391 documents in the WOS database and from 703 documents in the CNKI database. Based on the synergistic analysis of the life cycle model, it is found that the research on policies and regulations precedes the research on literature, and both are in the period of refinement.Based on the synergistic analysis using the co-occurrence comparison of subject terms in the crisis cycle model, it is found that there is a lack of research in the stages of prevention, monitoring, and governance, and this paper proposes the systematic governance mechanism and strategy for crisis resolution that conforms to the trend of life cycle evolution and is synergistic with policy and literature. This study has only selected Chinese policies and regulations, and the proposed governance strategies have not yet been verified in practice; future research can expand the scope and depth of the study and conduct empirical research and pilot projects.

Nonlinear analysis of the synchronous reference frame phase-locked loop under unbalanced grid voltage

The synchronous reference frame phase-locked loop (SRF-PLL), also called dqPLL, is an electric circuit commonly used in power electronics to estimate the phase angle of a three-phase AC grid. If the voltage is unbalanced, the PLL is modeled as a periodically forced nonlinear oscillator and is known in practice to converge to a steady oscillation. In the existing literature, the oscillation has been studied via linearization assuming a low level of unbalance. Aiming for stronger nonlocal statements, we present nonlinear analysis. We apply the method of autonomous comparison systems and incremental stability to show that the steady oscillation is unique and attractive in a wider neighborhood. Its lock-in domain is estimated using numerical phase portrait analysis. The oscillation is further approximated up to the terms of the second order in the unbalance factor—it yields an estimation of the time average of the PLL’s phase error which is not visible by linearization only. The results provide stability guarantees and can guide the tuning of SRF-PLL.

Energy-conserving neural network for turbulence closure modeling

In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning techniques to construct such models. However, the stability of machine-learned closure models and their abidance by physical structure (e.g. symmetries, conservation laws) are still open problems. To tackle both issues, we take the ‘discretize first, filter next’ approach. In this approach we apply a spatial averaging filter to existing fine-grid discretizations. The main novelty is that we introduce an additional set of equations which dynamically model the energy of the subgrid scales. Having an estimate of the energy of the subgrid scales, we can use the concept of energy conservation to derive stability. The subgrid energy containing variables is determined via a data-driven technique. The closure model is used to model the interaction between the filtered quantities and the subgrid energy. Therefore the total energy should be conserved. Abiding by this conservation law yields guaranteed stability of the system. In this work, we propose a novel skew-symmetric convolutional neural network architecture that satisfies this law. The result is that stability is guaranteed, independent of the weights and biases of the network. Importantly, as our framework allows for energy exchange between resolved and subgrid scales it can model backscatter. To model dissipative systems (e.g. viscous flows), the framework is extended with a diffusive component. The introduced neural network architecture is constructed such that it also satisfies momentum conservation. We apply the new methodology to both the viscous Burgers' equation and the Korteweg-De Vries equation in 1D. The novel architecture displays superior stability properties when compared to a vanilla convolutional neural network.

Predictor-based constrained fixed-time sliding mode control of multi-UAV formation flight

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicle formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational burden. Initially, using the Taylor series concept, we derive the predictive models for the fixed-time sliding surface and reaching law. Subsequently, we formulate a constrained performance index by integrating these predictions with the control input. Ultimately, the optimal control input is computed through solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop fixed-time system stability in the presence of external disturbances and constraints. Furthermore, the comparative analysis with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, an experimental test is conducted to demonstrate the algorithm's potential for hardware implementation by employing the Hardware-in-the-Loop test.

A stability governor for constrained linear–quadratic MPC without terminal constraints

This paper introduces a supervisory unit, called the stability governor (SG), that provides improved guarantees of stability for constrained linear systems under Model Predictive Control (MPC) without terminal constraints. At each time step, the SG alters the setpoint command supplied to the MPC problem so that the current state is guaranteed to be inside of the region of attraction for an auxiliary equilibrium point. The proposed strategy is shown to be recursively feasible and asymptotically stabilizing for all initial states sufficiently close to any equilibrium of the system. Thus, asymptotic stability of the target equilibrium can be guaranteed for a large set of initial states even when a short prediction horizon is used. A numerical example demonstrates that the stability governed MPC strategy can recover closed-loop stability in a scenario where a standard MPC implementation without terminal constraints leads to divergent trajectories.

Distributed Robust Learning-Based Backstepping Control Aided With Neurodynamics for Consensus Formation Tracking of Underwater Vessels.

This article addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Toward this end, graph theory is used to allow us to synthesize the distributed controller with a stability guarantee. Due to the fact that the parameter uncertainties only arise in the vessels' dynamic model, the backstepping control technique is then employed. Subsequently, to overcome the difficulties in handling time-varying and unknown systems, an online learning procedure is developed in the proposed distributed formation control protocol. Moreover, modeling errors, environmental disturbances, and measurement noises are considered and tackled by introducing a neurodynamics model in the controller design to obtain a robust solution. Then, the stability analysis of the overall closed-loop system under the proposed scheme is provided to ensure the robust adaptive performance at the theoretical level. Finally, extensive simulation experiments are conducted to further verify the efficacy of the presented distributed control protocol.

Stability Analysis of Switched Linear Systems with Neural Lyapunov Functions

Neural-based, data-driven analysis and control of dynamical systems have been recently investigated and have shown great promise, e.g. for safety verification or stability analysis. Indeed, not only do neural networks allow for an entirely model-free, data-driven approach, but also for handling arbitrary complex functions via their power of representation (as opposed to, e.g. algebraic optimization techniques that are restricted to polynomial functions). Whilst classical Lyapunov techniques allow to provide a formal and robust guarantee of stability of a switched dynamical system, very little is yet known about correctness guarantees for Neural Lyapunov functions, nor about their performance (amount of data needed for a certain accuracy). We formally introduce Neural Lyapunov functions for the stability analysis of switched linear systems: we benchmark them on this paradigmatic problem, which is notoriously difficult (and in general Turing-undecidable), but which admits existing recently-developed technologies and theoretical results. Inspired by switched systems theory, we provide theoretical guarantees on the representative power of neural networks, leveraging recent results from the ML community. We additionally experimentally display how Neural Lyapunov functions compete with state-of-the-art results and techniques, while admitting a wide range of improvement, both in theory and in practice. This study intends to improve our understanding of the opportunities and current limitations of neural-based data-driven analysis and control of complex dynamical systems.

TopoGCL: Topological Graph Contrastive Learning

Graph contrastive learning (GCL) has recently emerged as a new concept which allows for capitalizing on the strengths of graph neural networks (GNNs) to learn rich representations in a wide variety of applications which involve abundant unlabeled information. However, existing GCL approaches largely tend to overlook the important latent information on higher-order graph substructures. We address this limitation by introducing the concepts of topological invariance and extended persistence on graphs to GCL. In particular, we propose a new contrastive mode which targets topological representations of the two augmented views from the same graph, yielded by extracting latent shape properties of the graph at multiple resolutions. Along with the extended topological layer, we introduce a new extended persistence summary, namely, extended persistence landscapes (EPL) and derive its theoretical stability guarantees. Our extensive numerical results on biological, chemical, and social interaction graphs show that the new Topological Graph Contrastive Learning (TopoGCL) model delivers significant performance gains in unsupervised graph classification for 8 out of 12 considered datasets and also exhibits robustness under noisy scenarios.

Adjustable iterative Q-learning for advanced neural tracking control with stability guarantee

In this article, an accelerated Q-learning algorithm with evolving control is established to solve the optimal tracking control problem. First, an accelerated Q-learning scheme is constructed with an advanced Q-function. By utilizing the advanced Q-function, calculating of the feedforward control input can be avoided and the terminal tracking error can be eliminated. Then, by introducing the relaxation factor, the convergence rate of the iterative Q-function sequence is accelerated significantly, which is a potential way to diminish the computational burden. Furthermore, the convergence, positive definiteness, and stability conditions of the accelerated Q-learning algorithm are analyzed with some preconditions of the relaxation factor. Thus, the developed algorithm can achieve evolving control. Finally, the fantastic performance of the developed algorithm with critic network implementation is verified through two simulation examples.

Ultrasonic vibration cutting of advanced aerospace materials: a critical review of in-service functional performance

PurposeUnconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.Design/methodology/approachThe surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.FindingsTo address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.Originality/valueThis study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Direct learning of improved control policies from historical plant data

The continuous optimization of the operational performance of chemical plants is of fundamental importance. This research proposes a method that utilizes policy-constrained offline reinforcement learning to learn improved control policies from abundant historical plant data available in industrial settings. As a case study, historical data is generated from a nonlinear chemical system controlled by an economic model predictive controller (EMPC). However, the method’s principles are broadly applicable. Theoretically, it is demonstrated that the learning-based controller inherits stability guarantees from the baseline EMPC. Experimentally, we validate that our method enhances the optimality of the baseline controller while preserving stability, improving the baseline policy by 1% to 20%. The results of this study offer a promising direction for the general improvement of advanced control systems, both data-informed and stability-guaranteed.

TRANSFORMATION OF PUBLIC ADMINISTRATION MECHANISMS IN THE CONTEXT OF WAR

The article analyzes the transformation of public administration mechanisms in Ukraine caused by the ongoing military conflict with Russia. The authors sees the war as a catalyst for change that requires the Ukrainian government to adapt deeply and rapidly to new realities. The text describes in detail the challenges faced by Ukraine during the war, including the defense of its territory and sovereignty, ensuring the security of citizens, the impact of the war on the economy, and the humanitarian aspects related to millions of internally displaced persons.
 It is noted that Russia's attack was the largest military aggression in Europe since World War II, causing large-scale destruction and human casualties. The authors emphasizes the difficulties faced by Ukraine in protecting its citizens from Russian shelling, as well as the humanitarian crisis that has arisen as a result of the war. In particular, working with internally displaced persons in Ukraine in the context of the war with Russia requires flexibility, a comprehensive approach and active cooperation between all stakeholders. It is not only about providing temporary assistance, but also about creating conditions for a full life and social integration of internally displaced persons, as well as comprehensive social and psychological rehabilitation, which is a guarantee of stability and resilience of Ukrainian society in difficult times of war. It is emphasized that despite the challenges, Ukraine has demonstrated the ability to adapt and innovate, attracting international support and developing new strategies for managing the crisis. The authors examines how changes in the mechanisms of public administration are aimed at restoring and strengthening the economic and social structure of the country, as well as at ensuring national security and defense.

The control of superheater steam temperature in power plants using model predictive controller

The temperature of the steam produced by the superheater has been regarded as one of the most crucial parameters for steam power plant regulation. It must be precisely regulated within a restricted temperature range; overheating would result in the degradation of the substance. The recommended hybrid technique employed the Model Predictive Controller and Improved Fruit Fly Optimization Algorithm to regulate temperature levels and guarantee stability. Using the proposed method, model-based predictive control aims to enhance control quality and identify the optimal value. By employing this approach, the superheater can attain the optimal configurations for the steam temperature plant, thereby simultaneously enhancing the stability of the plant. The principal aim of this manuscript is to attain a reduction in the Integral Absolute Error to 40.6%. The simulation was conducted using the MATLAB/Simulink platform, and the outcomes demonstrate that the proposed method exhibits superior performance compared to those that have been documented in the literature.

An Automatic Control Perspective on Parameterizing Generative Adversarial Network.

This article presents a new perspective from control theory to interpret and solve the instability and mode collapse problems of generative adversarial networks (GANs). The dynamics of GANs are parameterized in the function space and control directed methods are applied to investigate GANs. First, the linear control theory is utilized to analyze and understand GANs. It is proved that the stability depends only on control parameters. Second, a proportional-integral-derivative (PID) controller is designed to improve its stability. GANs can be controlled to adaptively generate images by an overshoot rate that is only related to the PID control parameters. Third, a new PIDGAN is derived with a theoretical guarantee of stability. Fourth, to exploit the nonlinear characteristics of GANs, the nonlinear control theory is applied to further analyze GANs and develop a feedback linearization control-based PIDGAN named NPIDGAN. Both PIDGAN and NPIDGAN not only improve stability but also prevent mode collapse. With five datasets covering a wide variety of image domains, the proposed models achieve superior performance with 1024 × 1024 resolution compared with the state-of-the-art GANs, even when data are limited.

A Hybrid Submodular Optimization Approach to Controlled Islanding With Post-Disturbance Stability Guarantees

A Hybrid Submodular Optimization Approach to Controlled Islanding With Post-Disturbance Stability Guarantees

Salp Swarm Optimization-Based Approximation of Fractional-Order Systems with Guaranteed Stability

Salp Swarm Optimization-Based Approximation of Fractional-Order Systems with Guaranteed Stability

Достижение баланса интересов субъектов бюджетной деятельности в условиях бюджетно-правовых режимов

The analysis of a problem of achievement of balance of interests in the conditions of regime and legal budgetary regulation assumes characteristic of the dynamic nature of interest that expresses not only the nature of the purpose, the nature of intentions in activity of subjects, but also direct activities for emergence, change or the termination of the rights and duties. Coordination of interests of participants of any design of regime and legal regulation is defined by the system of participants of these legal relationship. In hierarchy of interests of society – the states – the territorial subject of the federation – public authority – the legal entity are, etc. possible and widespread options when the interests of one subject are implemented through implementation of interests of other subjects that does not exclude existence of independence of interests of everyone. The balance of income and expenses as guarantee of financial stability, assumes a rational ratio between the opportunities (which are expressed in the budgetary receipts) and the requirements (which are characterized by the budgetary expenses) between which balanced ratio – ideal option which at the same time assumes also certain deviations (deficiency or surplus of the budget).