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The current tendency toward increases in energy prices makes it necessary to discover new ways in which to provide electricity to end consumers. Cooperation among the various self-consumption facilities that form energy communities based on networked microgrids could be a more efficient means of managing the renewable resources that are available. However, the complexity of the associated control problem is leading to unresolved challenges from the point of view of its formulation. The optimization of energy exchanges among microgrids in the day-ahead electricity market requires the generation of an optimal profile for the purchase of energy from and sale of energy to the main grid, in addition to enabling the community to be charged for any deviation from the schedule proposed in the regulation service market. Microgrids based on renewable generation are systems that are subject to inherited uncertainties in their energy forecast whose interconnection generates a distributed control problem of stochastic systems. Microgrids are systems of subsystems that can integrate various components, such as hybrid energy storage systems (ESS), generating multiple terms to be included in the associated cost function for their optimization. In this work, the problem of solving complex distributed stochastic systems in the Mixed Logic Dynamic (MLD) framework is addressed, as is the generate of a tractable formulation with which to generate deterministic values for both exchange and output variables in interconnected systems subject to uncertainties using hybrid, stochastic and distributed Model Predictive Control (MPC) techniques.

System-of-systems (SoS) face major challenges, including heterogeneity, scalability, and intricate interactions among component systems (CSs). These systems typically operate in dynamic environments, introducing uncertainty and stochastic behavior. Many existing studies tend to oversimplify these complexities, with limited research focusing on supervising SoS under such conditions. Graph models, such as hypergraphs (HG), have been effective in modeling SoS organizations, while stochastic and weighted hypergraphs have been successfully used to handle stochasticity in other complex systems. In this article, we present the Multi-Level Stochastic Hypergraph (MLSHG) model, designed to address the challenges of modeling stochastic SoS. We also propose a novel algorithm for supervising large-scale SoS, integrating bottom-up monitoring with top-down reconfiguration to detect the addition or failure of CSs and manage the overall system’s capacity to achieve long-term objectives. In a case study on a mushroom harvesting SoS, the results showed that incorporating stochastic elements with an adaptive threshold enabled early reconfiguration, reducing deviations from the final goal. Additionally, the capability-based reconfiguration approach exhibited low computational time, with performance scaling linearly with the number of CSs, thus enhancing the system’s scalability.

Gait coordination (GC), meaning that one leg moves in the same pattern but with a specific phase lag to the other, is a spontaneous behavior in the walking of a healthy person. It is also crucial for unilateral amputees with the robotic leg prosthesis to perform ambulation cooperatively in the real world. However, achieving the GC for amputees poses significant challenges to the prostheses’ dynamic modeling and control design. Still, there has not been a clear survey on the initiation and evolution of the detailed solutions, hindering the precise decision of future explorations. To this end, this paper comprehensively reviews GC-oriented dynamic modeling and adaptive control methods for robotic leg prostheses. Considering the two representative environments concerned with adaptive control, we first classify the dynamic models into the deterministic model for structured terrain and the constrained stochastic model for stochastically uneven terrain. Inspired by the concept of synchronization, we then emphasize three typical problems for the GC realization, i.e., complete coordination on structured terrain, stochastic coordination on stochastically uneven terrain, and finite-time delayed stochastic coordination. Finally, we conclude with a discussion on the remaining challenges and opportunities in controlling robotic leg prostheses. [ABSTRACT FROM AUTHOR]

In this work, the problem of following references and rejecting disturbances that are contaminated with noise and whose dynamics are too complex to have a mathematical model that describes the behavior of such signals is studied. In addition to exploiting the capabilities of the Kalman filter (KF) to clean the signals so that they can serve as external signals for the regulation theory, the construction of the generating system (exosystem) is proposed as a diagonal system by blocks, where each block is a Kalman filter that estimates each one of the corresponding reference/disturbance signals. The viability of the approach is verified by two examples, mimicking the problem of tracking a flying object moving freely in the 3D space by a quadrotor which is under the influence of the proposed controller when the displacements of the arbitrary flying object are measured by low-cost sensors.

Non-cooperative games serve as a powerful framework for capturing the interactions among self-interested players and have broad applicability in modeling a wide range of practical scenarios, ranging from power management to path planning of self-driving vehicles. Although most existing solution algorithms assume the availability of first-order information or full knowledge of the objectives and others' action profiles, there are situations where the only accessible information at players' disposal is the realized objective function values. In this article, we devise a bandit online learning algorithm that integrates the optimistic mirror descent scheme and multi-point pseudo-gradient estimates. We further prove that the generated actual sequence of play converges a.s. to a critical point if the game under study is globally merely coherent, without resorting to extra Tikhonov regularization terms or additional norm conditions. We also discuss the convergence properties of the proposed bandit learning algorithm in locally merely coherent games. Finally, we illustrate the validity of the proposed algorithm via two two-player minimax problems and a cognitive radio bandwidth allocation game.

In this article, a finite-time adaptive fuzzy control scheme based on filter and reduced-order observer is proposed for induction motors (IMs) with load variation. First, the rotor position and the angular velocity of IMs are estimated by a reduced-order observer. Second, the unknown stochastic nonlinear functions are handled by the fuzzy logic systems. In addition, the finite-time control is combined with command filtering to solve the issue of “explosion of complexity” in the traditional backstepping method, and the errors compensation signal is introduced to reduce the filtering error, which can ensure the finite-time convergence and improve the robustness of the systems. The simulation and experimental results are given for validation of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article presents the $\epsilon$ -stealthy attack against cyber-physical systems under the stochastic communication protocol (SCP), which is used to avoid data collisions. The aim is to develop an optimal attack strategy to degrade the estimation performance maximally, while remaining a certain level of stealthiness characterized by the Kullback–Leibler divergence. Taking the SCP-introduced effects into account, a novel attack model is proposed, where only a part of the transmitted data is utilized to construct the attack signal at each time instant. Subsequently, the optimization problem is formulated. Different from the existing results, where only the numerical solution is given, the analytical expression of the optimal attack parameters is presented, where the analytical solution has more advantage in analyzing how the parameters affect the attack performance. Finally, the numerical simulations on a three-tank system and the experiments performed on a networked permanent magnet synchronous machine prototype system are provided to validate our results. [ABSTRACT FROM AUTHOR]

This article is concerned with the estimation problem of Toolface for dynamic point-the-bit rotary steerable drilling tool systems. First, considering the unknown frequency and amplitude of vibration during the drilling process, the Toolface system is modeled as a time-varying stochastic system with unknown and time-varying noise covariance matrices. Under the assumption that the noises and their covariance matrices, respectively, obey the Gaussian distribution and the inverse Wishart distribution, the variational Bayesian-based moving horizon estimation algorithm is proposed. Then, the state and the noise covariance matrices are inferred by iteratively updating their approximate posterior probability distributions. Finally, simulations and experiments are provided to demonstrate the effectiveness and superiority of the developed estimation scheme. [ABSTRACT FROM AUTHOR]

The quantized feedback stabilization problem is investigated for nonlinear hybrid stochastic time-delay systems with discrete-time observation in this article. A quantized discrete-time observation feedback controller is designed. The designed controller can make the $H_{\infty }$ stability and mean-square sense of the controlled system. Particularly, this note is the first to develop the stability criteria, which is not only related to the duration between two successive observations but also links to time lag of the system. By utilizing the Lyapunov method and inequality scaling techniques, the estimates for the duration and the time delay of the system are gained, respectively. Some case simulations are also provided to testify the proposed method and demonstrate its power. [ABSTRACT FROM AUTHOR]

This article is concerned with the stochastic stability and $l_{1}$ -gain control of two-dimensional (2-D) positive Markov jump systems (PMJSs) with directional delays based on the Roesser model. First, necessary and sufficient conditions (NSCs) for the stochastic stability of the addressed system are established by constructing a deterministic “equivalent” system and applying a stochastic copositive Lyapunov function. This reveals that the stochastic stability of 2-D PMJSs with delays is affected by the size of directional delays, the transition matrix, and system matrices. Second, the exact $l_{1}$ -gain index is calculated and NSCs in the form of linear programming (LP) are established for the addressed system. Systematic methods for the $l_{1}$ -gain controller design are proposed so that the closed-loop system (CLS) is positive and stochastically stable and has an optimal $l_{1}$ -gain performance, which is achieved using an iterative algorithm and an analytical calculation method for a single-input case. Finally, the potency and accuracy of the theoretical results are verified using two examples. [ABSTRACT FROM AUTHOR]

This article considers the problem of finite-time adaptive fuzzy switching event-triggered control for a class of nonaffine stochastic systems with periodic actuator faults and asymmetric error constraints. First, a framework of semiglobally finite-time stability in probability is established for stochastic systems. Then, unlike the existing observers, an adaptive state observer with faults compensation mechanism is designed to estimate unknown state. In order to balance the systems tracking performance and communication burdens, a switching event-triggered strategy is given to regulate trigger signal and avoids the Zeno behavior effectively. In backstepping process, a nonlinear tracking error-dependent function is constructed to constrain the tracking errors within asymmetric boundaries, and the effects of faults and trigger errors can be compensated completely by the designed finite-time fault-tolerant control strategy. Finally, the effectiveness of presented strategy is further verified by simulation results. [ABSTRACT FROM AUTHOR]

This technical note discusses the design process of the cooperative differential game (CDG) with the mixed $H_{2}/H_{\infty }$ control to obtain Pareto solutions for nonlinear stochastic systems by adaptive dynamic programming (ADP). In order to obtain Pareto solutions, a non-cooperative differential game (NCDG) for nonlinear stochastic systems is also studied. It is worth mentioning that the mixed $H_{2}/H_{\infty }$ control can achieve the expected $H_{2}$ performance under the condition of the $H_{\infty }$ continuous attenuation. Firstly, the two-player nonzero-sum differential game for stochastic systems is given to obtain Nash equilibrium. Then, on this basis, a joint cost function is established, which is derived from Pareto strategies and the corresponding Hamiltonian function. Furthermore, two critic neural networks (NNs) are used to realize Nash equilibrium and Pareto equilibrium, respectively. Finally, a detailed algorithm and a numerical example are given. [ABSTRACT FROM AUTHOR]

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example. [ABSTRACT FROM AUTHOR]

The integration of uncertain photovoltaics (PVs) and flexible loads leads to uncertainties in the power system dynamic simulation results. Furthermore, geographically close PV farms are correlated and may exhibit nonlinear correlations. This article proposes a copula-based sparse polynomial chaos expansion (PCE) framework for quantifying the impacts of uncertain dynamic PVs and loads on power system dynamic simulations and stability. The dynamics include both PV and load stochasticity and those governed by differential and algebraic equations. The copula statistics are utilized to accurately characterize the dependence structure of PVs and further used to develop the copula-PCE for quantifying the impacts of uncertain PVs and loads. A probabilistic TSI is also developed to assess the uncertainties from PVs and loads on the system stability. To address the cases, where both stable and unstable conditions coexist, a preprocessing step via sample classification is proposed. The effects of different dependence structures of PVs and different numbers of uncertain sources are investigated. Comparison results with other methods on the modified IEEE 39- and 118-bus systems, including the Monte Carlo method, Latin hypercube sampling, and traditional PCE without consideration of uncertain input correlations show that the proposed method is able to accurately quantify the uncertain dynamic simulations and transient system stability while being computationally efficient. [ABSTRACT FROM AUTHOR]

Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency. [ABSTRACT FROM AUTHOR]

This paper designs an optimal open-loop leader-follower control strategy of input-delay stochastic systems. The leader-follower control problem is described as an optimal problem based on Stackelberg strategies. A control strategy of the leader is given to minimize the function of $H_{2}$ norm, while a control strategy of the follower in worst case maximizing the function of $H_\infty $ norm is applied to the systems. Then the optimal leader-follower control strategy is obtained by establishing the information relationship of the forward and the backward variables with augmentation of the variables and solving several Riccati equations. At last, an example illustrates the effectiveness of the proposed strategy.

Nonstationary stochastic systems in the Wiener-Kolmogorov sense have properties defined by their moments of probability, entropy, and distribution function. Filtering Theory, in general, describes indirectly, a stochastic system through the processes of parameter estimation and state identification. The objective of this article is to develop a Recursive Filter with an Exponential Kernel (RFEK) to reconstruct the response of a nonstationary stochastic system. To achieve this, first, a system viewed as a Black Box (BB) is analyzed. These systems are those whose internal dynamics are unknown, only their input-output is known from a set of responses measured with respect to a particular excitation. From these measurements and applying the proposed filter, a set of estimated parameters and identified states are obtained as a characterization of the system. Subsequently, a comparison is made between the filter output signal and the reference signal over time; that is, measuring their point-to-point convergence. The convergence of the stochastic reference makes it possible to indirectly observe its stability from a bounded estimation region. As a case study, bioelectric signals of the electroencephalographic (EEG) type are analyzed giving an improved approximation with respect to the Kalman filter results.

Statistical model checking is a class of sequential algorithms that can verify specifications of interest on an ensemble of cyber-physical systems (e.g., whether 99% of cars from a batch meet a requirement on their functionality). These algorithms infer the probability that given specifications are satisfied by the systems with provable statistical guarantees by drawing sufficient numbers of independent and identically distributed samples. During the process of statistical model checking, the values of the samples (e.g., a user's car trajectory) may be inferred by intruders, causing privacy concerns in consumer-level applications (e.g., automobiles and medical devices). This paper addresses the privacy of statistical model checking algorithms from the point of view of differential privacy. These algorithms are sequential, drawing samples until a condition on their values is met. We show that revealing the number of samples drawn can violate privacy. We also show that the standard exponential mechanism that randomizes the output of an algorithm to achieve differential privacy fails to do so in the context of sequential algorithms. Instead, we relax the conservative requirement in differential privacy that the sensitivity of the output of the algorithm should be bounded to any perturbation for any data set. We propose a new notion of differential privacy which we call expected differential privacy (EDP). Then, we propose a novel expected sensitivity analysis for the sequential algorithm and propose a corresponding exponential mechanism that randomizes the termination time to achieve the EDP. We apply the proposed exponential mechanism to statistical model checking algorithms to preserve the privacy of the samples they draw. The utility of the proposed algorithm is demonstrated in a case study.

The issue of adaptive output-feedback stabilization is investigated for a category of stochastic nonstrict-feedback nonlinear systems subject to unmeasured state and unknown control directions. By combining the event-triggered mechanism and backstepping technology, an adaptive fuzzy output-feedback controller is devised. In order to make the controller design feasible, a linear state transformation is introduced into the initial system. At the same time, the Nussbaum function technology is used to overcome the difficulties caused by unknown control directions, and the state observer solves the problem of the unmeasured state. Based on the fuzzy-logic system and its structural characteristics, the issue of unknown nonlinear function with nonstrict-feedback structure in the system is tackled. The designed controller could not only guarantee all signals of closed-loop systems are bounded in probability but also save communication resources effectively. Finally, numerical simulation and ship dynamics example are given to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, the study of fault diagnosis and prognosis of discrete-event systems (DESs) has received considerable attention. This article aims to investigate the decentralized fault prognosis of stochastic DESs (SDESs). The notion of $m$ -step stochastic-coprognosability, called $S_{m} $ -coprognosability, is formalized to capture the capability of stochastic systems to predict a fault at least $m$ steps in advance under the decentralized framework. The verifier is constructed from the stochastic decentralized system, in which $n$ local prognosers are deployed to send the local prognostic decisions to a coordinator for calculating the final prognostic decision. In particular, a necessary and sufficient condition of $S_{m}$ -coprognosability of stochastic systems is derived, and an algorithm for testing $S_{m} $ -coprognosability is given, which is polynomial to the number of states and events but exponential to the number of local sites. Furthermore, the maximum number of $m$ for making the given SDES to be $S_{m} $ -coprognosable is discussed. Note to Practitioners—This article extends the fault prognosis methods of SDESs from centralized framework to decentralized framework, such that for many technologically complex systems whose information collection is scattered in physically separated different sites, every local site makes decision based on information collected by own sensors, without communicating with other local sites, which is more appropriate than the centralized method. [ABSTRACT FROM AUTHOR]

The $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ filter design for linear stochastic time-varying delay systems based on the filter velocity constraint is considered in this dissertation. Different from existing $\mathcal {L}_{2}\,-\,\mathcal {L}_{\infty }$ performance analysis and filter design, a new criterion is proposed to make a more accurate judgment, which can not only guarantee the stability of the filtering error system with $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ performance level but also can estimate the convergence speed of the filter. The ideal filter should has the same convergence speed as the original system, so as to ensure the rapid convergence of the error system, but also not to reduce the anti-interference ability and increase the bandwidth of the filter system. By the new filter design method, an ideal filter can be designed, which can regulate the convergence speed of the filter system to the desired effect. A simulation instance is provided to exhibit the validity of the new approach. [ABSTRACT FROM AUTHOR]

The output regulation approach has been extensively applied for the cooperative control of heterogeneous multiagent systems (MASs) with known dynamics, but rarely for MASs subject to stochastic and unknown dynamics. One challenging problem is that it is still unclear how to construct the regulator equations of stochastic MASs with unknown dynamics for dynamic exosystem compensation. Toward this end, this article develops a novel distributed control scheme for the approximate output regulation of discrete-time stochastic MASs subject to heterogeneous and unknown nonlinear dynamics. A distributed observer is designed for the exosystem-state estimation of agents, based upon which nonlinear regulator equations are constructed for the feedforward control design, thereby achieving distributed exosystem compensation. A high-order neural network is deployed to approximately solve the nonlinear regulator equations with unknown nonlinearity, and an adaptive control law is developed for the approximate output regulation of discrete-time stochastic MASs. Stability analysis shows that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation results demonstrate the effectiveness and efficiency of the proposed control law. [ABSTRACT FROM AUTHOR]

The stability issue of stochastic hybrid systems against energy-constrained denial-of-service (DoS) attacks is investigated in this article. A sampled-data-based Round-Robin protocol, which sends measurement data cyclically according to the predetermined transmission sequence, is introduced to avoid communication network congestion. Additionally, a new switched time-delay stochastic closed-loop system with an unstable subsystem is established by discussing the effect of DoS attacks. Then, the stability of the closed-loop system is discussed in light of the piecewise Lyapunov–Krasovskii functional method and stochastic analysis technique. Finally, two examples are included to illustrate the correctness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive neural-network (NN) tracking optimal control problem for stochastic nonlinear systems, which contain state constraints and uncertain dynamics. First, to avoid the violation of state constraints in achieving optimal control, the novel barrier optimal performance index functions for subsystems are developed. Second, under the framework of the identifier-actor-critic, the virtual and actual optimal controllers are presented based on the backstepping technique, in which the unknown nonlinear dynamics are learned by the NN approximators. Moreover, the quartic barrier Lyapunov functions are constructed instead of square ones to cope with the Hessian term to ensure the stability of the systems with stochastic disturbance. The proposed optimal control strategy can guarantee the boundedness of closed-loop signals, and the output can follow the given reference signal. Meanwhile, the system states are restricted within some preselected compact sets all the while. Finally, both numerical and practical systems are carried out to further illustrate the validity of the proposed optimal control approach. [ABSTRACT FROM AUTHOR]

A unified approach to fixed-time tracking control of stochastic high-order nonlinear systems (HONSs) with or without full-state constraints is studied in this article. By introducing two important universal-constrained functions and using coordinate transformation technology, the stochastic HONS with full-state constraints is transformed into an equivalent one without state constraints. Compared with the existing control schemes, the proposed scheme not only eliminates the feasibility conditions, but also accurately analyzes the scope of tracking error. Moreover, the limitation that the constraint functions need to be bounded is also removed; thus, the proposed scheme is a unified approach that can be applied to stochastic HONSs with or without state constraints. With the help of fixed-time stability theory and adaptive fuzzy control technology, a novel fixed-time fuzzy controller is designed. In addition, an adaptive event-triggering mechanism that the threshold parameters can be adjusted adaptively according to the tracking performance is introduced to reduce the communication burden. Simulation results verify the scheme developed. [ABSTRACT FROM AUTHOR]

The striking growth of powerful computing resources allows time-efficient solution of computationally demanding problems. In particular, advances in high-performance computing have made stochastic approaches to real-world applications more practical. The belief propagation (BP) algorithm is a probabilistic method typically used in information theory and artificial intelligence. This article exploits the probabilistic message passing attribute of BP for solving stochastic linear systems that naturally arise from finite element formulation of stochastic partial differential equations (PDEs), establishing an explicit connection between the two fields for the first time. The accuracy of the algorithm is validated by comparison to the well-known Monte Carlo method. [ABSTRACT FROM AUTHOR]

In this article, the adaptive bipartite containment control problem is investigated for stochastic nonlinear multiagent systems (MASs) with an event-triggered mechanism. It is known that the dynamic surface control method suffers from the mismatch between the virtual controller and the filter output. To address this issue, a novel error compensator is designed. Meanwhile, motivated by their universal approximation capability, fuzzy-logic systems (FLSs) are employed to identify the plants’ unknown nonlinear characteristics. To reduce the communication overhead, a distributed event-triggered control scheme is designed based on an estimate of unknown gain sign’s reciprocal. Leveraging the stochastic Lyapunov stability theory and backstepping design technique, it is proved that 1) the output responses of followers converge to a convex hull formed by those of the leaders and their symmetric ones; 2) all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability (SGUUBP); and 3) there is no Zeno behavior. Finally, simulation results are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The issue of fuzzy adaptive switching control for stochastic systems with arbitrary switching signal and finite-time prescribed performance is investigated in this article. A piecewise function is adopted to characterize finite-time prescribed performance, and the error signal is converted to a new state variable via the tangent function. Unknown functions are approximated via fuzzy-logic systems (FLSs). Based on the stochastic stability theory and common Lyapunov function, a fuzzy adaptive switching control scheme is presented. The control law is proposed for the stochastic switched closed-loop system so that not only all the signals are ensured to be semiglobally uniformly ultimately bounded (SGUUB) in probability but also a residual error related to the finite-time prescribed performance bound is guaranteed. Eventually, simulation studies for a practical system are given to show the effectiveness of the presented fuzzy adaptive switching control scheme. [ABSTRACT FROM AUTHOR]

This article is concerned with the stability analysis of time-varying hybrid stochastic delayed systems (HSDSs), also known as stochastic delayed systems with Markovian switching. Several easy-to-check and less conservative Lyapunov-based sufficient criteria are derived for ensuring the stability of studied systems, where the upper bound estimation for the diffusion operator of the Lyapunov function is time-varying, piecewise continuous, and indefinite. It should be stressed that our results can be directly used to analyze the stabilization of HSDSs via aperiodically intermittent control (AIC). Compared with the existing results about AIC, the restrictions on the bound of each control/rest width and the maximum proportion of rest width in each control period are removed. Thus, the conservativeness is reduced. Finally, two examples, together with their numerical simulations, are provided to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this article, the problem of intermittent fault (IF) detection is investigated for linear stochastic systems over sensor networks, where the appearing and disappearing times, and magnitude of IF are all nondeterministic. By utilizing the moving-horizon estimator, a novel residual generator is designed to realize the distributed detection of IFs in sensor networks. Different from the traditional moving horizon estimation algorithms, weight matrices of the quadratic cost function in this article are regulated by an unreliability index of the prior estimate to suppress the smearing effect of IFs. In virtue of the matrix transformation method and statistical theory, estimator parameters are obtained and the detectability of a single IF is analyzed by using the residual. In order to avoid the collisions of detection results from different residuals, the global detectability condition is given for all IFs. A cooperative decision-making strategy is proposed such that the only detection result can be guaranteed, which includes the appearing and disappearing times of IFs, and the nodes suffering from IFs. Finally, an illustrative example is provided to show the feasibility and effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

This study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi–Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and measurement noise are also considered. In contrast to the existing study works, there is a wider range of applications. An observer is explored to detect faults. A controller is studied to stabilize the considered system. A piecewise fuzzy Lyapunov function is collected to obtain delay-dependent sufficient conditions by means of linear matrix inequalities. The designed observer has less conservatism. In addition, the strict $(\mathfrak {Q},\mathfrak {S},\mathfrak {R})-{\epsilon }-$ dissipativity performance is achieved in the residual dynamic. Besides, the elaborate $H_{\infty }$ performance and the elaborate $H\_{}$ performance are also acquired. Finally, the availability of the method in this study is verified through two simulation examples. [ABSTRACT FROM AUTHOR]

Standardized radiation measurements can be simulated by various numerical or analytical approaches. In this article, a transfer function method is proposed for a fast and accurate antenna voltage prediction. Assuming that the interference source can be characterized as a linear Norton equivalent current source, its internal admittances and source currents are determined from measurements made with a vector network analyzer and an oscilloscope. The setup of the radiation measurement is described as a transfer function using its scattering parameters. A system of equations is set up with the multiport theory and solved for the antenna voltage, which is fed into a spectrum analyzer model. The result is compared to a measurement in a fully anechoic chamber. The method is extended to interference sources with stochastic signals, and, in the course of this, an approach for the estimation of the radiation measurement is developed. A dc motor is used as an application example. [ABSTRACT FROM AUTHOR]

Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computation delay suitable for real-time control under fading channel. [ABSTRACT FROM AUTHOR]

We study the stabilization problem for nonlinear stochastic systems via an event-triggered impulsive control (ETIC) scheme, where the impulsive control time sequence is generated by the event-triggered mechanism (ETM). Both continuous ETM and periodic ETM are developed by continuous measuring and periodic sampling, respectively. The continuous ETM with time regularization is proposed to exclude the Zeno behavior. The upper bound of the sampling period is given for the periodic ETM. By means of the continuous ETM and periodic ETM, sufficient conditions are given to guarantee the $p$ th moment uniform stability and the $p$ th moment exponential stability of related systems. Moreover, LMI-based conditions of exponential stability in the mean square are established for linear stochastic systems under ETIC. Finally, two examples are presented to illustrate the proposed ETIC schemes, in which an example of the consensus of linear stochastic multiagent systems is considered. [ABSTRACT FROM AUTHOR]

This article investigates the problem of adaptive fuzzy optimal distributed consensus control for stochastic multiagent systems (MASs) with full-state constraints and nonaffine nonlinear faults. Fuzzy logic systems are employed to identify the unknown nonlinearities. To solve the problem of optimal state constraint control, a barrier Lyapunov function based optimal cost function is designed. By introducing Butterworth low-pass filter into control design, the deleterious effects raised by nonlinear fault can be compensated. By utilizing adaptive dynamic programming algorithm in critic–actor construction, a fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semiglobally uniformly ultimately bounded in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are all not exceeded their constrained bound. Finally, simulation results are provided to illustrate the feasibility of the developed control method and theorem. [ABSTRACT FROM AUTHOR]

This article focuses on the design of fuzzy secure control for a class of coupled systems, which are modeled by a nonlinear $N$ -dimensional ($N$ -D) parabolic partial differential equation (PDE) subsystem and an ordinary differential equation (ODE) subsystem. Under stochastic deception attacks, a fuzzy secure control scheme is designed, which is effective to tolerate the attacks and ensure the desired performance for the considered systems. A new fuzzy-dependent Poincare–Wirtinger’s inequality (PWI) is proposed. Compared with the traditional Poincare’s inequality, the fuzzy-dependent PWI is more flexible and less conservative. Meanwhile, an augmented Lyapunov–Krasovskii functional (LKF) is newly constructed, which strengthens the correlations of the PDE subsystem and ODE subsystem. Then, on the ground of the fuzzy-dependent PWI and the augmented LKF, new exponential stabilization criteria are set up for the PDE-ODE coupled systems. Finally, a hypersonic rocket car is presented to verify the effectiveness and less conservatism of the obtained results. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered practical fixed-time containment control issue for stochastic nonlinear multiagent systems is addressed. The event-triggering mechanism is designed for the controller update which is reduced update frequency. In combination with the backstepping technique and fuzzy logic systems, an adaptive fuzzy containment control strategy is developed for stochastic uncertain multiagent systems. By adopting the protocol, an adaptive containment controller is devised, which can ensure that the containment errors gather to a small range in fixed time. Finally, simulation results for the practical example demonstrate the correctness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The sampled-data stabilization problem of nonlinear stochastic singular systems on the basis of the Takagi–Sugeno fuzzy models under variable samplings is discussed in this article. A new piecewise Lyapunov–Krasovskii functional is constructed, which can capture the actual sampling mode’s available features more fully, and an enhanced input-delay method is presented. By using the proper augmented scheme based on the auxiliary vector function, the new mean square admissibility criteria are derived by making good use of the convex combination techniques and the free weighting matrix approach. It is shown that the obtained results in this article contain less conservatism when compared with the existing ones. The superiority and correctness of our results are verified by an application example of a truck–trailer model. [ABSTRACT FROM AUTHOR]

This article presents a new sampled-data-based event-triggered pointwise security controller by pointwise measurements for partial differential equation (PDE) systems under stochastic cyberattacks. First, according to the Takagi–Sugeno fuzzy model, the considered nonlinear system is reconstructed and an event-triggered pointwise fuzzy controller is proposed with pointwise measurements. Moreover, networked control systems are introduced to improve the transmission convenience; however, two deception cyberattacks with different characteristics are brought into PDE systems for the first time. In addition, based on novel Lyapunov–Krasovskii functionals, some relaxed conditions to assure system’s exponential stability are established. Finally, the effectiveness and practicability of the designed controller are demonstrated by numerical and application examples. [ABSTRACT FROM AUTHOR]

This article focuses on the adaptive fast finite-time tracking control for a class of stochastic nonlinear systems. To begin with, an adequate qualification is provided for the practically fast finite-time stability. Then, based upon this adequate qualification, a new adaptive fuzzy control scheme is presented. Due to the stochastic disturbance, criteria of the existing fast finite-time stability cannot be applied to the analysis of the stability of stochastic nonlinear systems. To solve this issue, based on Lemma 6, an original criterion of the fast finite-time stability is constructed first in Lemma 7 by using the theorem of the integral mean value. Meanwhile, it would have a huge impact on the analysis of the fast finite-time stability for stochastic nonlinear systems. Finally, a simulation example is carried out for evaluating the validity of the main result. [ABSTRACT FROM AUTHOR]

In this article, the high-order nonlinear system is commonly studied in an underactuated weakly coupled mechanical system, the control design is difficult from the tractional control design for nonlinear systems. Thus, we study the finite-time fuzzy adaptive error constraint control problem for stochastic high-order nonlinear nonstrict feedback systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, a new error transfer variable is used to achieve the prescribed performance. Based on adding a power integrator technique and adaptive backstepping recursive control, a novel adaptive fuzzy finite-time prescribed performance control scheme is developed. By utilizing stochastically finite-time stable theory, the proposed control method can guarantee that the high-order system is semi-global finite-time stable in probability. Finally, both numerical and practical simulations are provided to verify the feasibility and effectiveness of the developed control method. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of adaptive event-triggered tracking control for a class of uncertain stochastic nonlinear systems in strict-feedback form. First, with the help of fuzzy logic systems to approximate the unknown nonlinear functions, a robust fuzzy state observer is constructed to estimate all the unmeasurable states. Next, an adaptive output feedback controller, which can adjust the variables online is designed by using the backstepping scheme. Concomitantly, in order to reduce the computation of communication process, a new event triggering condition involving the decreasing function of tracking errors is introduced. Moreover, the desired closed-loop stability of the resulting systems can be achieved by exploiting Lyapunov function analysis. Finally, simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]