This paper focuses on the output feedback control problem for fuzzy singularly perturbed systems (SPSs) with packet dropouts. Three Bernoulli random variables are introduced to describe the packet loss behaviors of different network channels between sensors and controllers. A time-scale-dependent decode-and-forward (TSDDaF) relay scheme is proposed to improve the remote transmission of the fuzzy SPSs, in which the slow and fast measurement signal can be decoded and reconstructed separately according to the different time scales. The key results of this article can be summarized in two aspects: (1) Firstly, by exploiting the received slow and fast signals from both the relays and sensors via different channels, a TSDDaF relay-based composite fuzzy output feedback controller is designed, under which the control performance of fuzzy SPSs can be effectively improved. (2) Secondly, by establishing a singular perturbation parameter-dependent Lyapunov functional, the sufficient conditions for exponentially ultimately boundedness (EUB) in mean square sense can be obtained and the numerical stiffness problem can be avoided. The feasibility of our approach is illustrated through a flexible joint inverted pendulum example.

This paper develops an adaptive fuzzy guidance strategy using zero-sum (ZS) differential games, incorporating event-triggered mechanisms to optimize data transmission while ensuring system stability. With the proposed guidance method, the missile can successfully engage a maneuvering target despite uncertainties and input constraints within the guidance system. Firstly, the nonlinear ZS differential game system is utilized to describe the mathematical model of interception guidance. Subsequently, an optimal control strategy is developed using game theory to ensure that the missile effectively captures the maneuvering target. To reduce unnecessary data transfers in the guidance process, an event-based sampling method is introduced as part of the control strategy design. Moreover, a generalized fuzzy hyperbolic model (GFHM) is adopted to approximate both the optimal cost function and the event-based robust optimal control strategy. To ensure convergence of the weight approximation errors, weight updating laws are established in accordance with the gradient descent method, where the requirement of an admissible initial control is relaxed by incorporating an additional function. Then, the stability of the closed-loop system is analyzed using Lyapunov functions, which demonstrates that the weight approximate errors are uniformly ultimately bounded (UUB). Finally, simulations involving a missile intercepting a maneuvering object are presented to support the developed control approach.

This paper investigates the joint design of multiple channel access and power control for multi-sensor remote estimation. Smart sensors with energy constraints transmit their local estimates over sharing Markovian fading channels. A novel discount-average weighting criterion (DAWC) is introduced in the infinite horizon, which balances immediate and long-term transmission performance, unlike traditional criteria that focus on one aspect. We formulate the co-design issue including channel selection and power allocation as a Markov decision process (MDP) with DAWC. The existence of epsilon-optimal policy is presented for ergodic MDP via a model checking method, and the switch-like optimal transmission policy is derived from the set of randomized Markov strategies. Further, we prove the existence of epsilon-s-optimal policy that is an ultimately deterministic policy for general MDP. An elaborately devised algorithm is employed to generate optimal transmission decisions utilizing a forward iterative approach. Finally, an example of turbofan engine speed regulation is applied to demonstrate the superiority of previous results.

In this paper, the asynchronously practical control is studied for discrete time switched systems with singular perturbations. Firstly, a novel Lyapunov function is constructed including the singular perturbation parameter and quasi-time parameter matrices. And then, quasi-time dependent criteria are achieved to study the practical stability and asynchronous stabilization; an allowable upper bound is expected to be obtained for the singular perturbation parameters; the asynchronous sampling controller is designed with quasi-time gains, and the application range of this controller is further widened via some constraints relaxed. At last, two simulation examples are utilized to illustrate that the proposed results are less conservative and effective.

This letter focuses on designing an optimal Markov transmission strategy for the remote estimation in renewable energy microgrids (REMs). To jointly account for estimation accuracy, transmission energy consumption, and future energy availability, a multiple discount weighting criterion (MDWC) is introduced as a unified performance index. The resulting sensor scheduling problem is formulated as a multiple-discount Markov decision process (MDP) under energy harvesting constraints, based on which the existence of an optimal Markov strategy is rigorously established. Furthermore, an explicit (N,infinity)-stationary optimal Markov strategy with a switch-like structure is derived. Ultimately, a practical example of REM is employed to verify the efficacy of the obtained results.

In this paper, a fixed-time adaptive control problem is investigated for constrained second-order multi-agent systems with unmatched uncertainties. To handle the state constraints, a constraint-handling transformation is introduced to guarantee the boundedness of system states during the control process. Based on the transformed scheme, a fixed-time observer is presented, which achieves the estimation of desired reference output for followers. Moreover, a nonsingular distributed fixed-time control protocol is developed by adding a power integrator technique and ensures the consensus tracking within a fixed time. Furthermore, an adaptive neural network approach is employed to compensate for the unmatched uncertainties without requiring exact system models. It is rigorously shown that all closed-loop signals remain bounded and that fixed-time consensus tracking is achieved while satisfying the imposed state constraints. Numerical simulation is provided to demonstrate the effectiveness of the proposed control strategy.

With the expansion and networking of power systems, distributed fusion estimation (DFE) has become integral to state monitoring. However, emerging cyber attacks, especially on data transmissions of wireless sensors, present significant risks to power system security. To evaluate attack impacts and strengthen defense resilience, this article investigates the security vulnerabilities in DFE of power systems, focusing on cyber-routing (CR) attacks with energy harvesting constraints. The CR attacker may corrupt some sensor measurements transmitted over wireless networks to local estimators, indirectly compromising the fusion estimate when aggregating the corrupted data. Aiming at maximizing the fusion estimation error covariance, a joint optimization problem is first established to comprehensively analyze the attack strategy, including target selection and action solving. To facilitate the execution of attacks, this issue is reformulated into a solvable suboptimization task utilizing the upper bound approximation technique. The optimal attack actions of compromised sensors and the attacking target set with a threshold-like structure are obtained via a two-stage solution approach. Additionally, an algorithm for generating attack targets is developed, applicable to both constant and time-varying energy harvesting conditions. Finally, all theoretical results have been illustrated by IEEE 39-bus power system.

This paper investigates the issue of distributed secure fusion estimation in power systems under stochastic event-based (SEB) attacks. A lossless data compression method utilizing full rank decomposition transform is proposed to reduce redundant sensor data, thereby alleviating network transmission load. To further save communication resources and prolong network lifetimes, sensors use a stochastic event-triggered mechanism (SETM) to transmit compressed data to local estimators. However, SETM can be exploited by SEB attackers. We first derive local estimator iterations using Bayesian inference in both normal and attack scenarios. Moreover, the accuracy of fusion estimates can be guaranteed through the codesign of information compensation and an indexed transmission strategy that counteracts SEB attacks. Finally, this paper provides an upper bound for fusing error covariance under a diagonal matrix weighted fusion scheme, effectively balancing fusion estimation performance and resource demands. All theoretical results have been illustrated by the 5-Generator and 8-Line power system.

In this paper, the practical input-to-state stabilization is studied for discrete time singularly perturbed switched systems under asynchronous switching. Quasi-time function dependent Lyapunov functions are founded to analyze the practical input-to-state stability of above systems. And then, quasi-time dependent stabilization criteria are achieved with less conservatism to design the asynchronous controller, by which, the practical input-to-state stabilization is realized for discrete time singularly perturbed switched systems. One simulation example is utilized to illustrate that the proposed results are less conservative and effective.

This paper is concerned with the practical control for discrete time singular perturbed switched systems via the dynamic event triggering mechanism. The quasi-time parameter matrices are introduced into the novel Lyapunov function and much freedom degree is added into the stability analysis. And then, less conservative stabilization criteria are achieved to design sampling controllers with quasi-time gains. At last, a numerical simulation example is given to illustrate the effectiveness of results.

This paper introduces a novel strategy, Multi-Stage Based Visual Servoing (MSBVS), to tackle the significant challenge of planning complex trajectories in robotic systems without hand-eye calibration, particularly in some assembly tasks. The MSBVS strategy decomposes a complete visual servoing process into three distinct phases, each characterized by unique functionalities. By assigning specific image features and control objectives to each phase, the strategy indirectly facilitates the planning and tracking complex trajectories. Furthermore, a novel image feature, the vanishing angle, is introduced, enhancing the precision in representing an object’s spatial position. Subsequently, a robotic vision system is established, and real-time simulations and experiments are performed to verify the efficacy of the MSBVS strategy. The results show that MSBVS efficiently plans and executes complex trajectories in robotic assembly tasks.

The product quality indicators of the penicillin fermentation process have multiple semantics and are interrelated. There is a complex nonlinear mapping relationship between input characteristics and multiple-output objectives, and the time dependence is strong. As a result, the prediction accuracy of existing soft sensor models is poor, and it is difficult to meet the needs of industrial sites. To solve the above problems, this paper proposes a multi-output soft sensor modeling method for the penicillin fermentation process based on big data feature analysis. In this method, the process data is divided into several batches in order, and then the data features of multivariable and time-dependent datasets are extracted according to the deep sparse self-coding neural network method to realize the effective mining of the relationship between multivariable time series factors, based on the multi-output support vector regression method, several soft sensor models for different prediction targets are established. Meanwhile, to improve the Predictive performance of the soft sensor model, the improved black hole algorithm is used to optimize the model parameters. Finally, a simulation experiment is carried out based on the simulation dataset of the penicillin fermentation process to verify the effectiveness of the proposed method.

• In this paper, the problem of stability analysis and L 1-gain characterization for uncertain Markovian hybrid switching positive systems is investigated. The key feature in the paper is that the switching mechanism is described by a general class of hybrid switching laws, which is comprised of Markovian switchings and deterministic switchings. • To develop the criteria ensuring the stochastically exponential stability and L 1-gain, multiple time-varying copositive Lyapunov functions are constructed by using the mode-dependent time interval segmentation technique. • Moreover, two special cases are also under investigation via our proposed method and two new results are provided. • Finally, a numerical example and a practical example are provided to illustrate the obtained results. In this paper, the problem of stability analysis and L 1-gain characterization for uncertain Markovian hybrid switching positive systems is investigated. The key feature in the paper is that the switching mechanism is described by a general class of hybrid switching laws, which is comprised of Markovian switchings and deterministic switchings. By using the mode-dependent time interval segmentation technique, multiple time-varying copositive Lyapunov functions are constructed. Then, the criteria ensuring the stochastically exponential stability and L 1-gain are derived. Finally, a numerical example and a practical one are provided to illustrate the obtained results.

This paper studies a problem of the sampled-data stabilization for Takagi-Sugeno fuzzy systems with actuator failures. An improved Lyapunov functional is constructed, which is dependent on the fuzzy membership functions FMFs and includes the sampling states with two sampling instants. A new integral term including the FMFs is introduced into this Lyapunov functional for the first time. Each term in this Lyapunov functional need not be positive, but it should be positive at sampling instants. The variation range of the FMFs is taken into account in dealing with the derivative of this Lyapunov functional as well. Then, improved stabilization criteria guaranteeing a larger sampling interval are obtained by applying the developed fuzzy controller. Simulation examples are given to illustrate the effectiveness and less conservatism of the proposed method.

This paper studies the sampled-data based asynchronous control problem for switched nonlinear systems subject to stochastic perturbations. Applying the T-S fuzzy model, the sampled-data based asynchronous stabilization is studied for switched nonlinear systems subject to stochastic perturbations. Combining the sampled-data dependent Lyapunov functional with the mode-dependent average dwell-time technique, a fuzzy controller is obtained to stabilize switched nonlinear systems in the mean-square sense. No more than one switching and multiple switchings are both discussed in one sampling interval to achieve more common results. At last, a simulation example about nonlinear mass-spring mechanical systems subject to stochastic perturbations is given to illustrate the effectiveness of proposed results.

This paper is concerned with the problem of event triggering H∞ synchronization control for discrete-time switched complex networks via quasi-time asynchronous controllers. One event triggering mechanism is introduced with the triggering parameters depending on triggering states. Applying quasi-time dependent multiple Lyapunov functions and the mode-dependent average dwell time technique, quasi-time dependent synchronization conditions are obtained with a lower weighted performance index. And then, a quasi-time dependent asynchronous controller is designed with quasi-time dependent control gains in each triggering interval. In addition, not only no more than one switching, but also multiple switchings are taken into account in each triggering interval, by which the assumption in some existing results is relaxed. Finally, the proposed result is applied to the PWM-driven boost converter.

This article is concerned with the quasi-time-dependent asynchronous Script capital H infinity filter design problem for a class of discrete-time switched systems via the event-triggering mechanism. Applying the quasi-time-dependent Lyapunov functions and the mode-dependent average dwell time technique, an asynchronous Script capital H infinity filter is designed with a weighted performance index; the filter parameter matrices are quasi-time-dependent in each event-triggering-dependent sampling interval; both cases (Case 1: no more than one switching, Case 2: multiple switchings) are taken into account in this sampling interval, by which the assumption, that the maximal asynchronous period is not larger than the minimal dwell time, is relaxed in this article. Simulation examples are given to show the less conservatism and effectiveness of the proposed results.

This paper is concerned with a novel Lyapunovlike functional approach to the stability of sampled-data systems with variable sampling periods. The Lyapunov-like functional has four striking characters compared to usual ones. First, it is time-dependent. Second, it may be discontinuous. Third, not every term of it is required to be positive definite. Fourth, the Lyapunov functional includes not only the state and the sampled state but also the integral of the state. By using a recently reported inequality to estimate the derivative of this Lyapunov functional, a sampled-interval-dependent stability criterion with reduced conservatism is obtained. The stability criterion is further extended to sampled-data systems with polytopic uncertainties. Finally, three examples are given to illustrate the reduced conservatism of the stability criteria.

This paper is devoted to investigating the $$H_{\infty }$$ filtering problem for Markov jump neural networks with hidden-Markov mode observation and packet dropouts, in which the information regarding to the Markov state can not be completely acquired. To address this circumstance, a hidden Markov model (HMM)-based technique is established. That is employing a detector to detect the information of the Markov state and then giving an estimated signal of the Markov state for the filter design. Some $$H_{\infty }$$ performance analysis criteria for filtering error systems and the corresponding HMM-based filter design procedure are given. An improved activation function dividing method (AFDM) is presented for neural networks to reduce the conservatism of the obtained results. The superiority of the improved AFDM and the validity of obtained results are verified by an illustrative example.