This paper explores the secure dynamic positioning problem under bidirectional deception attacks based on the newly proposed switched networked unmanned surface vessel (NUSV) model. A switched linear parameter-varying (LPV) model, accounting for mass variations due to changes in shipborne equipment, is established to characterize the NUSV dynamics. To counter bidirectional network attacks, a dual-channel deception attack defense mechanism is proposed. For the vessel-to-basal communication channel, the mechanism employs a controller mode estimator to provide reliable controller modes when data tampering is detected, thereby mitigating the duration of asynchronous behavior induced by deception attacks (DAs). For the basal-to-vessel channel, a quantity-parameter-based control input compensator is designed to counteract the impact of DAs on secure controller outputs. Based on this framework, a secure control scheme is developed to achieve robust dynamic positioning for NUSV. Finally, a numerical simulation is conducted to validate the efficacy of the proposed approach.

In unstructured environment, the robot faces the Precise Peg-in-Hole (PPiH) assembly as a non-cooperative issue. The posture uncertainty of the peg presents challenges in searching and inserting the hole. The purpose of the research is to eliminate the posture deviation between the peg and the hole with the force feedback. In this paper, the posture adjustment is divided into rough and fine processes. Firstly, for the rough adjustment, the force-angle samples from the end-effector are trained using a Multi-Layer Perceptron (MLP) model under peg-hole non-contact. The robot is guided by the MLP and adjusts the peg to roughly compensate for the posture deviation. Then, the robot brings the peg toward and contacts the hole. Secondly, for the fine adjustment, a Fuzzy-Finite Variable Admittance Control (FFVAC) model is established to estimate and adjust posture for different peg-hole contact states accurately. By integrating force information with fuzzy logic, the fuzzy inference system with fuzzy rules is developed based on the peg-hole contact states. According to the contact states, the twin delayed deep deterministic policy gradient (TD3) model finds the optimal admittance control parameters to achieve the surface close fitting of the peg and hole. Finally, comprehensive experiments are conducted under the unknown initial posture of the peg. The results are analysed by comparing with the stated of the arts of the posture adjustment methods regarding adjustment accuracy and operation time. The proposed method quickly reduces the posture deviation angle less than 0.2°, facilitates the following hole search and insertion works.

In this article, we tackle the problem of analyzing dissipativity, via devising switching mechanisms, for switched positive Takagi–Sugeno (T–S) fuzzy systems with time-varying delay. To leverage the positivity properties of state, input, and output variables, a novel concept of dissipativity is developed, focusing on linear supply rates and linear copositive storage functionals. When state information is available, the state dependent switching mechanism satisfying a dwell time constraint is introduced dependent on the constructed time-varying multiple linear copositive storage functionals. This mechanism allows for solving the dissipativity issue for the entire system without imposing any solvability requirements on subsystems and reduces the switching frequency. In cases, where state information is unavailable, dissipativity is ensured by a dwell-time dependent switching mechanism. Further, all conditions guaranteeing the solvability of the problem are presented in the form of linear vector inequalities. Two simulation examples are finally offered, demonstrating that the proposed techniques are effective and superior.

In recent years, the synchronization of coupled neural networks (CNNs) has been extensively studied. However, existing results heavily rely on assuming continuous couplings, overlooking the prevalence of intermittent couplings in reality. In this article, we address for the first time the synchronization challenge posed by intermittently CNNs (ICNNs) with coupling delay. To overcome the difficulties arising from intermittent couplings, we put forward a general piecewise delay differential inequality to characterize the dynamics during both coupled intervals and decoupled intervals. Based on the proposed inequality, we establish delay-independent synchronization criteria (DISCs) for ICNNs, enabling them to tackle general coupling delay. Notably, unlike previous studies, the achievement of synchronization in our approach does not rely on external control. Furthermore, for ICNNs that synchronize only under small delays, we formulate non-linear matrix inequality (LMI)-based delay-dependent synchronization criteria (DDSCs) that are computationally efficient and do not require delay differentiability. Finally, we provide illustrative examples to demonstrate our theoretical results.

In this article, a dynamic event-triggered adaptive antidisturbance (ETAAD) switching control strategy is proposed for switched systems subject to multisource disturbances. The disturbances are divided into two categories: the available unmodeled disturbance and the unavailable dynamic neural network modeled disturbance. First, a dynamic ET criterion is set based on the system state. Then, a novel dynamic ETA disturbance estimator is introduced to observe the modeled disturbance. Furthermore, according to the ET rule and adaptive disturbance observer, a switched controller is designed. Next, under the controller and switching criterion with the average dwell time limitation, sufficient conditions are given to force the switched systems to realize multisource disturbance suppression (DS), trajectory tracking, and communication resource (CR) saving simultaneously. Meanwhile, the Zeno phenomenon may be caused by the ET rule being excluded. In addition, the presented ETAAD approach is also applicable to the nonswitched systems case. Finally, a simulation case is given to validate the effectiveness of the dynamic ETAAD switching control method.

This article is concerned with the adaptive disturbance suppression output regulation (ADSOR) problem for the switched systems (SSs) subjected to multisource disturbances. The disturbances are consisted of the measurable unmodeled disturbance and unmeasurable dynamic neural network (DNN) modeling disturbance. First, an adaptive disturbance estimator (ADE) is designed to capture the unmeasurable disturbance. Then, via the ADE, the ADSOR controller, and the switching index are co-constructed to make the regulation on the system output and the suppression on the multisource disturbances of SSs. Further, for the SSs with multisource disturbances, the criteria are derived to force the OR performance as well as the multisource disturbance resistance performance, even though the subsystems are not individually capable of achieving these performances. Finally, the turbofan model simulation verifies the applicability and validity of the developed results.

This article concerns the finite-time performance analysis for switched Takagi–Sugeno fuzzy (STSF) systems subject to time-varying delay. Since the existing relevant results for finite-time synthesis of general switched systems require the finite-time stability property of the individual subsystem, a more general situation that the STSF systems comprised fully of finite-time unstable delayed subsystems are considered in the investigation. For surmounting this situation, a novel time-dependent multiple Lyapunov–Krasovskii functional approach is developed by integrating a ranged dwell time switching mechanism. Then, the corresponding (input–output) finite-time stability criteria with less conservativeness are simultaneously derived for the (perturbed) STSF delayed systems to be (input–output) finite-time stable over the concerned finite-time interval. Finally, two illustrative examples are provided to demonstrate the accuracy and superiority of the developed (input–output) finite-time analysis framework.

This investigation proposes an event-triggered-based antidisturbance switching control technique for the switched Takagi–Sugeno (T–S) fuzzy systems (FSs) subject to multiple disturbances as well as unavailable system state. The disturbances are comprised of two parts, i.e., the unavailable modeled disturbances and the available unmodeled disturbances. First, a composite observer is constructed to capture the unavailable system state and unavailable modeled disturbances. Then, on the basis of the observer, a switching controller with an event-triggered program inserted is established. Meanwhile, a switching criterion is formulated. Further, under the developed controller and switching criterion, the sufficient conditions are presented for the switched T–S FSs to achieve the multiple disturbances suppression and the communication transmission resource saving. In the end, the reasonability of the raised event-triggered-based antidisturbance switching control scheme is verified with the employed simulation example.

This study investigates the distributed event-driven filtering problem for a class of discrete-time systems in sensor networks (SNs) with switching topology. The addressed systems are considered to suffer from the unknown nonrandom perturbations with bounded peak, and thus the existing filtering or estimation approaches for switched SNs become inapplicable. To tackle this situation, a novel reachable-set-based distributed filtering strategy is established. With the proposed piecewise Lyapunov function approach and the minimum dwell time switching mechanism, sufficient conditions are then formulated for the existence of admissible filters in both the secure and nonsecure communication channels. We prove that the resultant filtering error is ultimately confined to a bounded closed set, which can also be minimized to achieve an optimal perturbation attenuation level in the sense of reachable set. Finally, the practicability and effectiveness of the developed design technique are demonstrated via the distributed event-driven filtering of a two-spring-mass mechanical system monitored by the SN.

This article addresses the problems of dissipativity analysis and feedback dissipativity of discrete-time switched positive systems with time-varying delay. On the basis of the positivity property of the investigated system, the definition of linear$(q,r) - \alpha $-dissipativity, utilizing the linear copositive storage functionals with linear supply rates, is first introduced. The switching among subsystems is orchestrated by a state-dependent switching policy that obeys prespecified dwell-time constraints. Second, in order to reduce the conservatism brought by the identical summation term functional for all positive subsystems, we propose a dwell-time-dependent linear copositive storage functionals involving summation terms which are diverse appearing in the functional. Then, less conservative criteria are developed to guarantee the system is strict$(q,r) - \alpha $-dissipativity by resorting to the switching policy or the joint implementation of the switching policy and time-varying controllers for subsystems. Finally, the validity of the proposed techniques is illustrated through two examples, and the relationship among the minimum dwell time, performance index, and time-varying delay is also revealed.

The present study concerns the dissipativity-based synchronization problem for the discrete-time switched neural networks with time-varying delay. Different from some existing research depending on the arbitrary and time-dependent switching mechanisms, all subsystems of the investigated delayed neural networks are permitted to be nondissipative. For reducing the switching frequency, the combined switching paradigm constituted by the time-dependent and state-dependent switching strategies is then constructed. In light of the proposed dwell-time-dependent storage functional, sufficient conditions with less conservativeness are formulated, under which the resultant synchronization error system is strictly (~X,~Y,~Z) - ϑ -dissipative on the basis of the combined switching mechanism or the joint action of the switching mechanism and time-varying control input. Finally, the applicability and superiority of the theoretical results are adequately substantiated with the synchronization issue of two discrete-time switched Hopfield neural networks with time-varying delay, and the relationship among the performance index, time delay, and minimum dwell time is also revealed.

This article is concerned with the event-triggered finite-time$H_{\infty }$estimator design for a class of discrete-time switched neural networks (SNNs) with mixed time delays and packet dropouts. To further reduce the data transmission, both the measured information of system outputs and switching signal of the SNNs are only allowed to be accessible for the constructed estimator at the certain triggering time instants. Under this consideration, the simultaneous presence of the switching and triggering actions also leads to the asynchronism between the indices of the SNNs and the designed estimator. Unlike the existing event-triggered strategies for the general switched linear systems, the proposed event-triggered mechanism not only allows the occurrence of multiple switches in one triggering interval but also removes the minimum dwell-time constraint on the switched signal. In light of the piecewise Lyapunov–Krasovskii functional theory, sufficient conditions are developed for the estimation error system to be stochastically finite-time bounded with a finite-time specified$H_{\infty }$performance. Finally, the effectiveness and applicability of the theoretical results are verified by a switched Hopfield neural network.

synchronization problem for a class of switched coupled neural networks subject to exogenous perturbations. Different from the existing results on the nonswitched and continuous-time control cases, the unmatched phenomena between the switching of the system models and that of the controllers will occur, when the resulting error system switches within a sampling interval. In the framework of time-dependent switching mechanism, sufficient conditions for the existence of the sampled-data controllers are derived under the variable sampling and asynchronous switching. We prove that the proposed method not only renders the synchronization error system exponentially stable but also constrains the influence of the exogenous perturbations on the synchronization performance at a specified level. Finally, a switched coupled cellular neural network and a switched coupled Hopfield neural network are provided to illustrate the applicability and validity of the developed results.

In this article, the energy-to-peak state estimation problem is investigated for a class of switched neutral neural networks subject to the external perturbations with bounded energy. Both the values of the measurement outputs and switching signal of the subsystems are only available for the controllers at the discrete sampling instants. Unlike the results for nonswitched neural networks, the coexistence of the switching and sampling actions directly causes the asynchronous phenomena between the indexes of subsystems and their corresponding controllers. To address this situation, the piecewise time-dependent Lyapunov-Krasovskii functional and slow switching mechanism are introduced. Under the developed theorem conditions, we prove that the designed state estimator exponentially tracks the true value of the neural state with the accessible sampled-data information. Also, the influence of the exogenous perturbations on the peak value of the estimation error is constrained at a prescribed level. Finally, a neutral cellular neural network with switching parameters is employed to substantiate the effectiveness and applicability of the theoretical results.

This study is concerned with the event-driven synchronization for discrete-time switched complex networks. To mitigate the transmission frequency, the dynamic event-triggered mechanism is introduced to orchestrate information transmission. In addition, the investigated complex networks are subject to the unknown nonrandom perturbation with bounded peak, and thus, conventional approaches do not apply, and new approaches are required. For handling this situation, a novel reachable-set-based synchronization technique is then established. With the dwell time switching strategy, sufficient conditions with less conservativeness are formulated, under which the synchronization error is attracted exponentially to a bounded closed region for any initial conditions. Alternatively, for some specified initial sets, the synchronization error is constrained permanently in a bounded closed set. Finally, numerical simulations substantiate the effectiveness and applicability of the theoretical results.

This article investigates the problem of input-output finite-time estimation for a class of discrete-time complex networks with switching topology. The dynamic event-triggered communication manner is proposed to conduct the data transmission of the switching signal and measurement outputs subject to random sensor nonlinearities. The asynchronism of the switching and triggering actions also leads to the received switching index by the designed estimator being inconsistent with that of the estimated switched complex networks. Then, a novel piecewise Lyapunov functional, which is proved to be less conservative than the general one, is then constructed for handling the input-output finite-time estimation under the unmatched circumstance. Sufficient conditions are formulated to ensure that the underlying estimation error systems are input-output finite-time stochastically stable with respect to the norm bounded inputs within a finite time. Finally, the superiority and applicability of the developed approach are verified by a simulation example.

This article investigates the issues of passivity analysis and feedback passification for a class of switched Takagi-Sugeno (T-S) fuzzy systems with the sampled-data-dependent switching strategy and controllers. Different from the previous switching laws, the proposed switching law only requires the values of system state at the discrete sampling instants. More incisively, a dwell time constraint for all subsystems is produced, which reduces the switching frequency and avoids the occurrence of chattering phenomenon or Zeno behavior. Then, sufficient conditions for the existence of the sampled-data-dependent switching strategy and controllers are formulated, under which the switched T-S fuzzy systems is strictly passive without requiring the strict passivity of any subsystem. In addition, the storage function is only decreasing with respect to its value at the sampling times, which in turn implies that the storage function can be nonmonotonous. Finally, a numeral example and a room air regulating system are employed to demonstrate the effectiveness and applicability of the presented theoretical results.

-gain analysis for a class of delayed master-slave chaotic neural networks subject to actuator saturation. Based on a switching strategy, the synchronization error system is modeled as a switched synchronization error system consisting of two subsystems, and each subsystem of the switched system satisfies a dwell time constraint due to the characteristics of intermittent control. A piecewise Lyapunov-Krasovskii functional depending on the control rate and control period is then introduced, under which sufficient conditions for the exponential stability of the constructed switched synchronization error system are developed. In addition, the influence of the exogenous perturbations on synchronization performance is constrained at a prescribed level. In the meantime, the intermittent linear state feedback controller can be derived by solving a set of linear matrix inequalities. More incisively, the proposed method is also proved to be valid in the case of aperiodically intermittent control. Finally, two simulation examples are employed to demonstrate the effectiveness and potential of the obtained results.