This brief introduces a fully distributed fault-tolerant control scheme based on a dual-channel dynamic event-triggered mechanism (DETM) for multi-agent systems (MASs) affected by both external disturbances and actuator faults, aiming to achieve leader-following bipartite consensus. To reconstruct unknown system states, external disturbances, and actuator faults, a series of observers is developed utilizing only local output measurements. Based on these estimates, a fully distributed fault-tolerant control law is formulated to ensure leader-following bipartite consensus, while simultaneously enhancing robustness against disturbances and resilience to faults. To alleviate communication burdens, dual-channel DETMs are implemented on both the sensor-to-observer (S-O) and observer-to-controller (O-C) channels, where internal dynamic variables are employed to adaptively adjust the triggering thresholds. The efficacy of the proposed method is validated via simulations studies on robotic manipulator systems.

This paper addresses a dual-layer bumpless transfer control (DLBTC) problem for Markovian jump systems (MJSs), aiming to achieve smooth transitions during stochastic mode switchings. First, the design of a multi-controller structure necessitates a system mode-dependent Markov chain, which enriches system dynamics and enhances robustness. However, it design inevitably increases stochastic mode switchings and aggravates bump phenomena. Second, to suppress these bumps, a dual-layer bumpless transfer control strategy is developed that constrains both input smoothness and rate continuity. Meanwhile, the traditional reference matrix mechanism is replaced by a weighted coefficient mechanism to improve adaptability across operating scenarios. Then, sufficient conditions on stochastic stabilization and DLBTC performance of MJSs are established via linear matrix inequalities. Finally, the impact of multi-controller structure and the proposed DLBTC on stochastic stabilization and transient behavior is demonstrated through an RLC circuit and a 40K engine control system as case studies.

This study focuses on the model reference adaptive tracking control problem for uncertain switched systems using a dynamic event-triggered approach. Compared with the existing studies, the switching adaptive laws and switching adaptive tracking controller are dynamically event-triggered simultaneously for data transmission, leading to several challenges in achieving the tracking task and Zeno-free behavior. A multiple Lyapunov function method is established to design a switching event-triggered adaptive law and switching dynamic event-triggered adaptive tracking controller. The proposed dynamic event-triggered mechanism guarantees that the interexecution time between two consecutive triggered points has a positive lower bound. The state-dependent switching signal and switching dynamic event-triggered adaptive tracking controller are designed such that all the signals of the error dynamic system are bounded, and the system state asymptotically tracks the desired reference model state. Finally, an example of an electro-hydraulic system illustrates the availability of the established strategy.

The issue of adaptive dynamic event-triggered asymptotic tracking control for strict-feedback nonlinear systems with unknown functions and full-state prescribed-performance constraints is discussed in this paper. A novel self-adjusting performance function (SAPF) is constructed by merging a continuous function with a finite-time performance function. By associating a transformation function and SAPF, the state constraints problem is recast into analyzing the boundedness of the new variables. Moreover, a new dual dynamic variable-dependent event-triggered mechanism is provided to reduce redundant data transmission. By using a command-filter technique and neural network method, a control scheme is proposed to guarantee the system output asymptotically tracks the reference signal and all system states satisfy specified constraints. Lastly, an applied example is introduced to illustrate the effectiveness of the proposed scheme. Note to Practitioners—In reality, many practical systems, such as flexible manipulators and unmanned underwater vehicles, need to operate in a constrained region. Motivated by this, we design controller for nonlinear systems to guarantee steady-state and transient performance. To reduce the conservatism, a novel SAPF is constructed and then a prescribed performance control strategy is proposed, which can not only achieve the asymptotical tracking of the nonlinear system, but also ensure the constraint performance. In addition, to decrease the transmitted data via communication network, a new dual dynamic variable-dependent event-triggered mechanism is proposed. This method has been illustrated to be feasible via a simulation example.

In this article, the issue of bumpless transfer hybrid non-fragile finite-time$H_\infty$control for Markovian jump systems (MJSs) is investigated, where the transition rates are partially available. The non-fragile strategy with tolerating both additive and multiplicative perturbations is designed, which greatly relaxes the application scope of the traditional controller. The bumpless transfer control idea is introduced to depict the transient behavior caused by a jumping controller. First, a bumpless transfer constraint condition is provided to restrict the amplitude of the hybrid non-fragile jumping controller, for which the additive and multiplicative perturbations are considered. Then, a bumpless transfer hybrid non-fragile controller is developed to guarantee the solvability of the finite-time$H_\infty$control issue for MJSs with partially available transition rates. Finally, an electronic circuit system example is applied to illustrate the usefulness of the proposed bumpless transfer hybrid non-fragile control approach.Note to Practitioners—This article is motivated by the finite-time hybrid non-fragile$H_\infty$control issue of MJSs with the bump limitation constraint. The bumpless transfer is often encountered in the Markovian jumping control field because it is usually impossible to implement unlimited control signals or bumpy control signals and is a main source of instability, and degradation of performance of MJSs. The traditional bumpless transfer technique because of the constant control gain cannot handle effectively the non-fragile bumpless transfer problem for MJSs. In this paper, the restriction of the constant control gain is relaxed. We develop a bumpless transfer control strategy considering a strong non-fragility of the control gain. In contrast to the existing results, a non-fragile bumpless transfer can tolerate the co-existing additive and multiplicative perturbations with a wider application. This study presents the method for practitioners interested in bumpless transfer controller design.

The asymptotic tracking control problem is investigated for nonstrict-feedback switched nonlinear systems by developing a dynamic event-triggered adaptive control technology. A key contribution is to design a hysteresis-type switching rule and an event-triggering-based switching controller to realize the asymptotically convergent target. First, by utilizing the Nussbaum-type function technique, the obstacle caused by unknown nonsmooth saturation nonlinearity is circumvented. Second, a novel gain-dependent switching signal is constructed, which ensures a time interval between any adjacent switching. Furthermore, a dynamic event-triggered schema is established, which allows the occurrence of asynchronous switching phenomenon generated by switching and event-triggering. An event-triggering-based switching controller is designed so that the adaptive asymptotic tracking control problem is solvable even when input saturation and vanishing control gains arise. Finally, simulation results with theoretical analysis are presented to validate the feasibility of the control algorithm. Note to Practitioners—With the development of artificial intelligence techniques, the studies of switched nonlinear systems have received widespread attention. This investigation is motivated by the vanishing control gains problem for switched nonlinear systems. We propose a co-design of a hysteresis-type gain-dependent switching rule and an event-triggering-based switching controller for switched nonlinear systems under input saturation and vanishing control gains. In existing results, the designed control signal is required to enter into the systems everywhere, while we relax the restriction. An improved dynamic event-triggered adaptive control framework has been established to obtain less conservative conditions further, where the control gain is allowed to vanish at some points. This study presents the method for practitioners interested in adaptive control design.

This study investigates bipartite containment control algorithm for multi-agent systems which are vulnerable to DoS attacks and external disturbances. Disturbances are generated via a series of exogenous nonlinear systems with a one-sided Lipschitz condition. To estimate unknown system states and external disturbances, a set of decentralized state observers and disturbance observers is proposed. Then a fully distributed control protocol is adopted to avoid the utilization of global information. An attack compensator is introduced to mitigate the adverse impacts of DoS attacks. Furthermore, Zeno-free dynamic event-triggered mechanisms that do not require continuous communication between neighboring agents are presented to conserve limited communication resources. In the end, satellite flight systems are introduced to illustrate the feasibility of the designed control scheme.

In this study, the problem of mean square exponential $l_{2}-l_{\infty }$ control is investigated for switched-Markovian jump systems (SMJSs). SMJSs are subject to deterministic switching obeying mode-dependent average dwell time (MDADT) and stochastic switching complying to Markov chain. mode-dependent transition probability (MDTP) and edge-dependent transition probability (EDTP) are proposed. MDTP describes the transition probability (TP) of Markovian jump systems (MJSs) under deterministic switching, and EDTP portrays the TP among MJSs affected by deterministic switching, which is associated with two different deterministic switching modes. Using multiple discontinuous Lyapunov function technology, the mean square exponential stability with $l_{2}-l_{\infty }$ performance is guaranteed by the MDADT method, MDTP and EDTP. Certain solvable sufficient conditions are obtained for the controller. Finally, two numerical examples and a practical example are provided to illustrate the validity of the obtained results.

This study discusses the problem of event-triggered (ET) asymptotic tracking control for parametric strict feedback nonlinear systems (SFNSs) with time-varying disturbances and unknown control directions. A unified dynamic threshold method is proposed by combining a unified function with a self-adjustable performance function. In contrast to previous research, the results of this study provide a unified framework in which global or semi-global performance can be achieved through simple parameter selection while excluding the conservativeness of the constraint thresholds owing to the artificial selection of a uniform performance function. The basic lemma based on the Nussbaum function frequently is extended to adapt to the case in which the coefficients are multiple bounded functions. By fusing a first-order differentiator, the reduplicative derivation of the virtual controller in the backstepping process is obviated. Moreover, an ET mechanism with two dynamic variables is constructed to reduce the burden of data transmission. The developed controller can guarantee the boundedness of all signals in the closed-loop system, full-state constraints performance, and asymptotic tracking control performance. Finally, the feasibility of the proposed scheme is attested by two examples.

This article investigates the bumpless transfer (BT)$H_{\infty }$control problem for a class of switched linear systems. The key point is that a novel BT control framework exploiting controllers collaborated with switching signal (SS) design is proposed. This is based on the consideration that for most existing BT control strategies, the design of SS is only used to guarantee the stability of switched systems instead of the BT performance, while the BT performance is achieved by adding additional limitations to the original controllers, which makes it impossible to achieve both stability and BT performance simultaneously. A continuous switching controller with time-varying gain matrices is developed, which achieves continuous transition on the switching instants. A generalized time and state-dependent SS is designed, in which the dwell time provides enough regulation time to pursue a desired BT performance, while, the state-dependent switching relaxes the requirement on each system by allowing each subsystem to be unstable, which helps to realize a satisfactory steady-state performance. Besides, a sufficient condition verifying the solvability for the BT$H_{\infty }$control problem by the designed controller and SS is constructed. Finally, to demonstrate the availability of the proposed BT$H_{\infty }$control strategy, two examples are provided.

This work proposes an adaptive neural network asymptotic tracking control strategy, capable of ensuring flexible fixed-time prescribed transient behavior for switched nonlinear systems with external disturbances and input saturation. Unlike most existing prescribed performance controls with input constraints that ignore the contradiction in fulfilling the input constraint and the output constraint, the proposed strategy realizes a trade-off between them by constructing a modified fixed-time prescribed performance function. The performance function boundary will increase with the error between the actual input and the desired control input, which can sacrifice the user-specified performance while saturation exists and fix it while no saturation exists, lowering the risk of singularity. Together with the designed performance function, a Zeno-free event-triggered controller is built to enforce that the output error converges asymptotically and satisfies the performance function boundaries within a fixed time. Besides, the complexity explosion problem is circumvented through the command-filtered technique. Finally, two case studies are provided to validate the established control strategy.Note to Practitioners—Due to physical restrictions, there are various system input and output constraints in practical engineering, which severely hampers the application of both traditional and cutting-edge control algorithms, particularly in the chemical, boiler, robot, and other sectors. Regrettably, the existing approaches only consider input and output constraints separately and assume that they can be implemented simultaneously, which is unrealistic in many practical systems. Hence, this work focuses on developing a novel adaptive fixed-time prescribed performance control strategy by introducing a modification signal, which can not only comprehensively balance input and output constraints but also achieve the asymptotic tracking of the system output. Meanwhile, an event-triggered mechanism is introduced to deal with the problem of limited communication bandwidth.

This article focuses on the adaptive anti-disturbance bumpless transfer (AADBT) control issue for the switched neural network systems (SNNSs) encountered by the unmeasurable neural network (NN) modeled disturbance and the measurable unmodeled disturbance through the multiple Lyapunov functions method. First, an adaptive regulator is set to appropriate the unknown parameter in the NN disturbance model. Then, an adaptive disturbance observer is built to estimate the NN modeling disturbance. Further, based on the adaptive regulator and disturbance observer, a switched controller and a state-relevant switching regulator are dual designed to compensate the influence of the NN modeled disturbance and alleviate the influence of the unmodeled disturbance with the jumps in the control input are reduced. Finally, the established control scheme is applied to the switched RLC circuit model, exhibiting the effectiveness of the control strategy.

This article introduces an adaptive dynamic event-triggered technique for addressing the output tracking control problem of uncertain switched nonlinear systems with prescribed performance. First, a switching dynamic event-triggered mechanism (SDETM) is established to alleviate network burden and conserve computational resources. A notable aspect is the inclusion of asynchronous switching between the switching subsystems and controllers. Second, a state-dependent switching law ensuring a dwell-time constraint is designed, which avoids the frequent switching phenomenon within any finite time interval. Third, an SDETM and an adaptive dynamic event-triggered controller are developed to confine the output tracking error within predefined decaying boundaries, while ensuring that all the signals of the closed-loop switched system remain within bounded regions. Finally, the validity and applicability of the developed control scheme are demonstrated through a one-link manipulator example.

The present study proposes an adaptive fuzzy tracking control strategy for switched nonlinear systems, capable of effectively addressing false data injection (FDI) attacks and input saturation, while achieving flexible prescribed performance control as well as semi-global uniform ultimate boundness for the resultant system. Compared to the previous work, the provided control strategy exhibits two notable strengths: 1) it introduces a novel modified fixed-time pregiven performance function to effectively balance input saturation and output constraint and 2) the detrimental impacts resulting from FDI attacks are successfully mitigated by implementing the fuzzy logic systems approximation technique in the backstepping procedure. A set of switching fuzzy observers are established to estimate the unobservable states while a first-order differential filter is utilized to handle the complexity explosion problem. Finally, the mass-spring-damper system is given to substantiate the developed approach.

This article studies the$H_\infty$control problem for fuzzy switched cyber-physical systems (FSCPSs) subject to denial-of-service (DoS) attacks. An observer state-dependent switching signal is designed using the multiple Lyapunov function technology under the unavailable system states. When DoS attacks occur, the information transmission involving the sensor and controller data is corrupted. Under the designed switching signal, the subsystem with the lowest energy is selected in both the DoS sleeping interval and the DoS activated interval. First, a switching technique is introduced to deal with a DoS attack behavior. The$H_\infty$control problem of the considered FSCPS is divided into two cases for handling: the presence and absence of DoS attacks. Then, a fuzzy switching observer and an observer-based fuzzy switching controller are proposed to guarantee the solvability of the$H_\infty$control problem for FSCPSs with DoS attacks. Finally, a simulation example demonstrates the effectiveness and usefulness of the proposed fuzzy switching control strategy for FSCPSs subject to DoS attacks.

In this article, a novel adaptive self-triggered controller is constructed to handle the fault-tolerant control problem for nonlinear system with input dead zone through a funnel function method: 1) the actuator failure and the dead-zone nonlinearity are fully analyzed for the modeling purpose, we transform the system into a new model; 2) the self-triggered scheme is set up to update the control signals, which effectively economizes communication resources; 3) due to the existence of unknown control gains and unknown nonlinear functions, the adaptive neural network funnel controllers are designed, such that the tracking error is confined within the funnel boundary and all the signals of the closed-loop system are semi-globally uniformly ultimately bounded; and 4) finally, the applicability of the acquired control algorithm is demonstrated via a rigid robot manipulator system.

The security problem for a kind of switched nonlinear systems with denial-of-service (DoS) attacks is addressed here with a novel event-triggered neural network (NN) adaptive control technique. The provided event-triggered control algorithm, compared to the current output feedback control schemes on continuous-time systems, not only conserves communication resources but also defends against DoS attacks throughout the system stabilization process. First, a novel adaptive NN switching observer is proposed by examining the stability of each subsystem in the sleep and active intervals of the attack, which makes the standard backstepping controller workable even in the DoS attacks scenario. Further, an event-triggered secure control strategy is established by creating a common coordinate transformation, which ensures the bound of all the signals in the resultant system under arbitrary switching and prevents the occurrence of Zeno behavior. A first-order filter is developed to fix the complexity explosion problem in backstepping design. Finally, a continuous stirred tank reactor example is presented to support the established method.

This article investigates the finite time bumpless transfer fuzzy control problem for Markovian jump fuzzy systems (MJFSs) via an observer-based bumpless transfer fuzzy control approach. Neither the measurability of system states nor the availability of all transition rates for each subsystem is required. A bumpless transfer index with a continuous reference input is derived to characterize the transient jumping behavior of MJFSs caused by an observer-based fuzzy jumping controller. First, a Markovian jump fuzzy model is employed to describe the more general practical situations. Second, by introducing a fuzzy state observer, an observer-based bumpless transfer fuzzy jumping controller is established, and a sufficient condition is developed such that the finite time fuzzy control issue for MJFSs is solvable while ensuring the bumpless transfer index. Finally, two simulation examples are provided to verify the utilizability of the acquired 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.

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

This article focuses on the bumpless transfer$H_{\infty }$anti-disturbance control problem for switching Markovian LPV systems under a hybrid switching law. A parameter-dependent multiple piecewise disturbance observer-based bumpless transfer control strategy is put forward to reject multiple disturbances and reduce switching bumps. First, a hybrid switching law making full use of determinacy and randomness is proposed to improve the bumpless transfer anti-disturbance level by introducing a fixed dwell time in random switching. Second, a generalized bumpless transfer anti-disturbance specification is given to describe the switching quality at the switching points of switching Markovian LPV systems. Third, a solvability condition is established for the bumpless transfer$H_{\infty }$anti-disturbance control problem, and a parameter-dependent multiple piecewise disturbance observer-based bumpless transfer controller is designed. Finally, an application example has been supplied to demonstrate the availability of the developed method.

This article investigates the$H_{\infty }$stochastic tracking control problem for uncertain fuzzy Markovian hybrid switching systems by using a fuzzy switching dynamic adaptive control approach. The long and the short is to construct multiple piecewise stochastic Lyapunov functions which provide an effective tool for designing hybrid switching law and fuzzy switching dynamic adaptive law. A hybrid switching law, including both stochastic switching and deterministic switching, is designed to represent more general switching scenarios, which can improve the$H_{\infty }$adaptive tracking performance through offering a running time before stochastic switching for the adaptive control strategy to work well. A fuzzy switching dynamic adaptive control technique is developed such that all signals of the tracking error equation are bounded, and the system state trajectory tracks the reference model state trajectory under a disturbance attenuation level as closely as possible. Finally, an application study verifies the effectiveness of the acquired methods.

This article focuses on the$H_{\infty }$adaptive tracking problem of uncertain switched systems. A key point of the study is to set up a multiple piecewise Lyapunov function framework which provides an effective tool for designing an adaptive switching controller consisting of a state-feedback and time-driven switching signal and a time-driven adaptive law. The proposed switching signal guarantees the solvability of the$H_{\infty }$adaptive tracking problem for uncertain switched systems. Significantly, it provides plenty of adjusting time for the adaptive tracking control strategy to damp the transient caused by switching and avoids frequent switching. A novel time-driven adaptive switching controller is established such that the tracking error asymptotically converges to zero and all the signals in the error dynamic system are bounded under an achieved disturbance attenuation level. The solvability criterion ensuring an$H_{\infty }$adaptive tracking performance is established for the uncertain switched systems, where the solvability of the$H_{\infty }$adaptive tracking problem for individual subsystems is not required. Finally, the proposed method is applied to the electro-hydraulic unit.

This article addresses the problem of adaptive finite-time neural tracking control for nonstrict-feedback nonlinear systems via dynamic surface (DS) technique. First, a new quasi-fast finite-time practical stability (QFPS) criterion is proposed for a class of general nonlinear systems. Then, the presented QFPS criterion is applied to design the desired adaptive finite-time neural tracking controller for a class of nonstrict-feedback nonlinear systems. The presented design scheme for the nonstrict-feedback nonlinear system has the following two features: 1) the “explosion of complexity” issue of the backstepping design is addressed by utilizing the DS technique and 2) the structural feature of Gaussian functions is applied to solve the design difficulties caused by the nonstrict-feedback form. It is proved that the designed controller for the nonstrict-feedback nonlinear system can make the resulting closed-loop system stabilizable in a quasi-fast finite time and the tracking error converges to a sufficiently small neighborhood of the origin. Finally, the simulation results are given to show the validity and practicability of the proposed design scheme.

In this article, a decentralized adaptive finite-time tracking control scheme is proposed for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. First, a practical almost fast finite-time stability framework is established for a general nonlinear system, which is then applied to the design of the large-scale system under consideration. By fusing command filter technique and adaptive neural control and introducing two smooth functions, the "singular" and "explosion of complex" problems in the backstepping procedure are circumvented, while the obstacles caused by unknown interconnections are overcome. Moreover, according to the framework of practical almost fast finite-time stability, it is shown that all the closed-loop signals of the large-scale system are almost fast finite-time bounded, and the tracking errors can converge to arbitrarily small residual sets predefined in an almost fast finite time. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed finite-time decentralized control scheme.

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples.

This paper investigates dissipativity and related properties for switched linear parameter-varying (LPV) systems by using parameter and dwell time-dependent multiple storage functions, which are different from traditional multiple storage functions because of the introduction of the external parameters into the storage functions construction. First, a novel concept relating to external parameters is proposed to describe the overall dissipativity property of switched parameter-varying systems in the absence of the conventional dissipativity property of the subsystems. Second, a state and parameter-triggered and time-driven switching laws is proposed, which avoids Zeno behavior by guaranteeing a dwell time among the adjacent switching. Third, several classic forms of dissipativity are addressed. For passivity and L2-gain, under certain negative output feedback asymptotic stability is guaranteed with the aid of asymptotic zero state detectability. Finally, the effectiveness of the proposed result is illustrated by its application to a speed adjustment problem of an aero-engine.