This paper explores the stabilization control problem of nonlinear systems with time-varying input delay in the presence of unknown sensor sensitivity. Different from existing methods, we consider the effects of large sensor sensitivity and input delay on the system, in which the sensor sensitivity can be described as an unknown time-varying function with an arbitrarily large bound. To address this, we propose a truncated predictive output feedback control method based on the classification of unknown sensor sensitivity. Firstly, by designing the truncation constant, a truncation classification method of sensor sensitivity is established, dividing the sensitivity into two categories: small sensor sensitivity and large sensor sensitivity. Secondly, a novel Lyapunov-Krasovskii functional is constructed to deal with the impact of truncated prediction and large sensor sensitivity classification on system stability under time-varying input delay. Then, based on the constructed observer, a truncated predictive controller is designed. Within the framework of the new Lyapunov-Krasovskii functional analysis, and with the aid of Lyapunov stability theory, it is strictly proved that under the action of the output feedback law, all states of the system can converge to zero regardless of the sensor sensitivity. Finally, the effectiveness of the proposed method is verified by a simulation example.

This paper studies the global finite-time control problem for uncertain high-order nonlinear systems (HNSs) with event-triggered input and deferred output constraint. The bounds of control coefficients are not required to be known and odd rational powers are allowed in the system. In this case, unlike the existing adaptive estimation control results can only achieve bounded or asymptotic stability, the proposed control strategy focuses on ensuring finite-time stability (FTS). Specially, two sets of distinct power-type parameters are introduced in control process to reconstruct the adaptive law and integral-type candidate Lyapunov functions respectively, such that the residual terms containing uncertainties can be dominated by the stabilizing terms. To reduce communication burden, an event-triggered mechanism is developed with a state-dependent function instead of a constant, enabling a timely update for controller as all state variables reach zero. Based on Lyapunov analysis and FTS theory, it is proven that the deferred output constraint can be guaranteed and all state variables reach the origin in a finite time under the designed controller. Two simulation examples are illustrated to verify the validity of theoretical results.

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems (MASs) under output event-triggered communication. A novel distributed discontinuous backstepping control strategy is presented, which utilizes information exclusively from intermittent output instants. First, a distributed adaptive event-triggered mechanism (ETM), along with a continuous-discrete time compensator and observer are designed jointly to compensate for consensus errors and reconstruct the system state, where all parameters designed are flexibly chosen. The triggering sampling instants are asynchronous and aperiodic, eliminating the necessity for continuous neighbor information monitoring. Second, we introduce a dynamic variable into the coordinate transformation to overcome the challenge of intermittent output in backstepping design, which is the key to address input errors caused by triggering mechanisms. Compared with the widely used backstepping method using the first-order filter to address intermittent signals, the proposed scheme does not require additional triggering design for the filter and achieves a full-state consensus in the global sense. Theoretical analysis shows that the system is asymptotically stable, all consensus errors converge to zero, and Zeno behavior is excluded. Finally, simulation examples are presented to demonstrate the effectiveness of the theoretical result.

This paper delves into the distributed consensus control problem of nonlinear multiagent systems under the influence of actuator deception attacks based on a fixed directed topology. Diverging from the existing research, we develop a new actuator deception attack model, where attack signals are generated by an unmodeled system satisfying the input-to-state stable condition, and the unmodeled system utilizes the output consensus error of the agents and its delayed error information as the system input. In this condition, we put forward a novel distributed output feedback consensus control approach. First, we design the distributed controller with a compensator for the follower by the use of the relevant outputs of the agents, which is independent of the time delay and the states of the unmodeled system. Then, by constructing a new Lyapunov function with an adjustable power parameter, we can regulate the range of the functions describing the false data injected into the actuator. Additionally, through the combination of the exchange supply function method, we establish a strict proof that all agents can achieve exponential leader-following full-state consensus driven by the given controller. Finally, a simulation example is presented to demonstrate the effectiveness of the developed approach.

The global asymptotic stabilization control algorithm is proposed for interconnected nonlinear systems utilizing intermittent output feedback. A dynamic double side event-triggered mechanism (ETM) is designed to make the available output intermittent, reducing the frequency of signal updates. In this case, we relax some restrictive conditions from related studies. The considered system features unknown time-varying parameters, mismatched uncertainties, and uncertain functions that satisfy nonlinear growth conditions. These complexities render the standard backstepping recursive design scheme inapplicable, as the derivative of the virtual controller does not exist. To address the intermittent output feedback problem, we introduce a novel dynamic backstepping control method. First, we establish a dynamic gain observer using the triggered output signals to reconstruct the unmeasurable state variables. Next, the concept of dynamic gain is introduced through a coordinate transformation, with its derivative employed to offset discontinuous terms, which solves the challenges in recursive backstepping design caused by intermittent output and regulates that the state variable converges asymptotically to the origin in the global sense. Final, the simulation example is proposed to show the validity of the developed algorithm.

This article explores the distributed leader-following full-state consensus control problem for feedforward nonlinear stochastic multiagent system with actuator deception attacks under a fixed directed topology. Unlike the existing works, we establish a novel actuator deception attack model, the attacks have higher stealthiness in model. The false injection information of the attack is generated by a stochastic inverse dynamics system satisfying the stochastic input-to-state stable condition, and the input of the system depends on the relevant output information of neighboring agents, rather than all state information. In this case, we develop a novel distributed output feedback consensus control algorithm to overcome the impact of attacks and stochastic disturbances. First, we design the distributed linear controller with a compensator for each follower based on the relevant outputs, and provide sufficient conditions for ensuring its effectiveness. Then, we construct a new candidate Lyapunov function with a power constant that is used to relax the constraints of the attack model. Subsequently, by means of the exchanging supply function approach, we strictly prove that all agents can achieve leader-following full-state bounded consensus in probability. Finally, an illustrative simulation is provided to showcase the efficacy of our methodology.

This article focuses on the fully distributed dual-event triggered leader-following consensus problem of heterogeneous multiagent systems (MASs) with unknown leader input and actuator fault. A hierarchical triggered control framework is developed for the MASs. First, in the upper network layer, event-triggered observers are designed to reconstruct the leader’s information by using the states exchanged intermittently among the neighboring observers. Then, in the lower physical layer, an adaptive fault-tolerant controller is designed, whose update instant can be directly computed based on the received upper layer information. This not only has the potential to further reduce the update frequency of controller, but also avoid the continuous monitoring on measurement errors. Besides, utilizing this control framework can prevent the faults from being propagated along the communication network. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

This article concentrates on the event-triggered control problem for interconnected nonlinear systems under hybrid stochastic faults. Unlike existing results, our work considers both, sensor stochastic faults and process stochastic faults, which are referred to as hybrid stochastic faults. First, under hybrid stochastic faults, an observer is constructed to estimate all states, which converts the process stochastic faults into a processable form. Second, an event-triggered mechanism is proposed for the estimated states to reduce their update frequency. To deal with the problem that the triggered estimated states are nondifferentiable, a continuous controller with continuous estimated states is developed, and the event-triggered controller with triggered estimated states is established accordingly. By the use of Lyapunov stability theory, it can be rigorously shown that all signals of the closed-loop systems are bounded in probability. The proposed approach is illustrated with the simulation of two interconnected pendulums.

This work examines the distributed leader-following consensus problem of feedforward nonlinear delayed multiagent systems involving directed switching topologies. In contrast to the existing studies, we focus on time delays acting on the outputs of feedforward nonlinear systems, and we permit that the partial topology dissatisfy the directed spanning tree condition. In the cases, we present a novel output feedback-based general switched cascade compensation control method that addresses the above-mentioned problem. First, we put forward a distributed switched cascade compensator by introducing multiple equations, and we design the delay-dependent distributed output feedback controller with the compensator. Subsequently, when the control parameters-dependent linear matrix inequality is met and the switching signal of the topologies obeys a general switching law, we prove that the established controller can render that the follower's state asymptotically tracks the leader's state by employing an appropriate Lyapunov-Krasovskii functional. The given algorithm allows output delays to be arbitrarily large and increases the switching frequency of the topologies. A numerical simulation is presented to demonstrate the practicability of our proposed strategy.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

The issue of event-based asymmetric predetermined performance control (PPC) has been addressed for$p$-normal nonlinear systems with time-varying unknown control coefficients. A set of Nussbaum functions is introduced, capable of addressing both single and multiple unknown control coefficients. To achieve asymmetric PPC, a switching constraint scheme is proposed. Subsequently, an adaptive fuzzy controller based on dynamic events is developed. Improved techniques for approximating the unknowns of a system using fuzzy logic systems, while dynamic events are employed to reduce the frequency of controller updates. Using Lyapunov stability theory, it is proven that all signals in the closed-loop system remain bounded, and the tracking error is confined within the specified asymmetric boundaries. Eventually, the effectiveness of the present scheme is demonstrated by the simulation of three examples.

In this article, the adaptive backstepping control problem is investigated for a class of mismatched uncertain nonlinear systems with input and state quantization. All available states are generated by the static bounded quantizers, which can cause the failure of the recursive backstepping design. Previous results are based on linear-like virtual controllers to ensure that the partial derivatives of virtual controllers are constants, therefore, the systems are required to be an integral form or to meet matched conditions. Based on a dynamic gain approach, this article presents a new compensation mechanism to solve the difficulty of recursive backstepping design caused by discontinuous states, the control problem is transformed into a design problem of the dynamic variable. First, the dynamic variable is introduced based on a coordinate transformation, its derivative is used to compensate for discontinuous mismatched nonlinear terms. Then, with the help of the Lyapunov stability theorem, it is strictly proved that all signals of the closed-loop system are globally uniformly bounded. Finally, numerical simulations are provided to validate the effectiveness of the developed algorithm.

In order to improve the efficiency of data transmission and save communication resources, the problems of double event-triggered control are investigated for a class of high-order nonlinear random systems. Under more general system conditions, in addition to overcoming the difficulty of recursive design caused by signal discontinuity, the effects of high-order nonlinearity and random disturbances also need to be addressed. Based on the adding power integral technique, a practical finite-time stable result is established for the nonlinear random systems under a double event-triggered mechanism (ETM) and proved that there is no Zeno phenomenon. Compared with the existing results, the update frequency of the signals is effectively reduced, and the upper bound of the stable error is independent of trigger parameters, thus can be made sufficiently small by tuning design parameters. Furthermore, the result is expanded to finite-time stabilization, state variables converge to the origin in a finite time. Finally, numerical simulations verify the effectiveness of the proposed algorithm.

This paper focuses on the adaptive output feedback control problem for nonlinear stochastic systems with unknown measurement sensitivity based on dynamic event-triggered mechanism (ETM). Different from the existing works, a novel adaptive output feedback control algorithm is proposed for unknown measurement sensitivity (its sign and bounds are unknown) by means of Nussbaum-type function. First, a reduce-order dynamic gain K-filter is proposed to reconstruct the unmeasurable state variable. Second, a tangent-type barrier Lyapunov function with a predefined-time performance function is established to constrain system output into the given region in a predefined time. Third, a dynamic ETM is put forward to reduce trigger times, and then the controller is designed accordingly. Based on the Lyapunov stability theory, it is proved that system state variables converge to zero in probability and other signals of the closed-loop system are bounded in probability. Finally, the validity of the proposed algorithm is demonstrated by the numerical simulation on a single-link manipulator.

This article studies the global prescribed-time stabilization problem for a class of time-delay nonlinear systems with uncertain parameters. First, we design two time-varying gains with special properties, in which one is introduced into virtual controllers to achieve prescribed-time convergence and the other one is used to construct the Lyapunov-Krasovskii (L-K) functional and Lyapunov function to handle the nonlinear time-delay term and unknown parameters, respectively. Then, by utilizing double time-varying gains and the scaling-free backstepping design approach, a dynamic state feedback controller is constructed, which guarantees that all state variables reach zero within a prescribed time, and the prescribed time can be specified in advance. Then, based on new functionals and regular differential inequality, we figure out the explicit expression for the upper bound of all variables, which plays an important role in proving the boundedness of all system variables. Final, a simulation example is given to demonstrate the effectiveness of the proposed method.

This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. Output information for all agents, except for state information, can be acquired based on sensor measurement. Moreover, the stochastic disturbances from external unpredictable environments are considered on all agent systems with a feedforward structure. In these conditions, we propose a novel consensus scheme with a simple design procedure. First, for each follower, we construct a dynamic gain-based switched compensator using its output and its neighbor agents' outputs to provide feedback control signals. Then, for each follower, we develop a compensator-based distributed controller that is not directly associated with the topology switching signal such that it has a first derivative and antishake. Thereafter, by means of the Lyapunov stability theory, we verify that the leader-following consensus can be acquired asymptotically in probability under the controllers' action if the topology switching signal fulfills an average dwell time condition. Finally, the feasibility of the control algorithm is checked via numerical simulation.

This article considers the tracking control problem for an uncertain feedback nonlinear system with asymmetric time-varying full-state constraints and unknown control directions. We propose a new low-computation full-state constrained robust control algorithm, that removes the feasibility conditions of virtual controllers and solves the unknown control direction problem without using the Nussbaum gain technique. By introducing nonlinear transformation functions, the original constrained systems are converted into new unconstrained tracking error systems, and the new systems eliminate the limitation of the initial conditions. Then, to seek the correct control directions, an orientation function with error conversion is constructed, which avoids introducing Nussbaum-type functions and logic switching rules. The proposed method possesses inherent robustness against model uncertainties and disturbances, and guarantees that the full-state constraints and the tracking error of systems enter into a prescribed set in a fixed time. Finally, simulation examples are presented to demonstrate the superiority and effectiveness of the developed control algorithm.

This article concerns with the adaptive fixed-time control problem for classes of nonlinear cascade systems with parametric uncertainty. The majority of existing finite/fixed-time control strategies for uncertain nonlinear systems can only make system states approach to the neighborhood of origin over a finite/fixed time. Different from these results, we put forward a time-varying gain-based control algorithm to ensure that all system states return to the origin in a fixed time. With the help of the backstepping method, a time-varying adaptive controller is designed for a high-order nonlinear systems subject to uncertain parameters. Through constructing appropriate dynamic gain-based Lyapunov functions and utilizing our proposed stability criterion, it is proved that all states of the uncertain system can be adjusted to the origin in a fixed-time interval. Moreover, the settling time is not depend on the initial conditions of system and can be preset according to the actual requirements. Finally, the simulation results are provided to illustrate the effectiveness of the developed dynamic time-varying feedback control algorithm.

In this paper, global event-triggered adaptive prescribed tracking output feedback control is investigated for a class of nonlinear uncertain systems with unknown control direction. Different from the existing works, the design of the tracking error-dependent normalized function and prescribed boundary is based on our uniformly defined blow-up function rather than a specific function, and the asymmetric constraint requirements on tracking error can be achieved by appropriately selecting the blow-up function. By utilizing the Kalman filter ($K$-filter) and dynamic gain technique, a new reduce-order observer is proposed, which can make the estimated error exponentially converge to zero. In addition, by introducing a dynamic signal into the trigger condition, a novel event-triggered mechanism is proposed, which can extend the trigger time interval and completely counteract the event-triggered errors in terms of asymptotic stability by sacrificing part of the system transient performance. Furthermore, an adaptive controller is designed based on the backstepping method, which ensures the boundedness of the state of the closed-loop system, while regulating the tracking error meets the global prescribed performance requirements and approaches to zero asymptotically. Two examples are given to illustrate the effectiveness of the proposed control scheme.

This article is concerned with the fixed-time prescribed tracking control problem for the uncertain stochastic nonlinear systems subject to input quantization and unknown measurement sensitivity. Different from existing results, the sensitivity on the sensor for measuring the system state is considered as an unknown parameter instead of the known one. Due to unknown measurement sensitivity on the sensor, the real system state cannot be obtained by measurement; hence, we put forward a new feedback control algorithm by the use of the unreal measured value of the system state. Moreover, the fixed-time prescribed performance on the output tracking error is investigated by developing a novel performance function. By means of the backstepping method, an adaptive quantized controller is designed for the system. Based on the Lyapunov stability theory, it is proved that the controller can render the output tracking error that satisfies the fixed-time prescribed performance and all signals of the resulting closed-loop system are bounded in probability. Finally, simulation results are provided to illustrate the effectiveness of the proposed control algorithm.

This article considers the distributed output-feedback consensus control problem for nonlinear multiagent systems subject to input delays. Different from the existing related works, the input delay of each agent is described as an unknown time-varying function and is different from each other in this article. To deal with this problem, for each follower, we first construct a novel distributed observer based on the relative output information to asymptotically estimate the state information of the leader, then we introduce a classical observer to asymptotically estimate the state information of the follower based on its output information. By means of two observers, the leader-following consensus problem is transformed into the stability problem of the nonlinear system with unknown input delays. Subsequently, the distributed controller independent of delays is proposed for each follower by the use of the truncated prediction method under some conditions. Based on the Lyapunov stability theory, it is strictly proved that the distributed controller can render all agents achieving consensus. Finally, the effectiveness of the theoretical results is illustrated on the basis of numerical simulations on a group of single-link manipulators.

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.

This article focuses on the leader-following consensus problem for a class of high-order uncertain nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks and actuator faults under the directed topology. To reduce the network communication bandwidth resource from the controller to the actuator and mitigate the adverse effect from DoS attacks, a novel event-triggered control strategy is proposed based on the reliable attack detection mechanism. The general attack detection mechanisms depend on the residuals between the values from the systems and observers. Different from the general attack detection mechanisms, a novel attack detection mechanism is proposed based on the logic relationship of voltage level signals which from the outputs of system components. Besides, the controller is designed based on the backstepping method and the controller can guarantee that the Zeno behavior is excluded. Furthermore, by using the Lyapunov stability theory, it proves that the controllers make the MASs achieve consensus. Eventually, a simulation example is presented to demonstrate the effectiveness of the proposed theoretical results.

This paper concerns the predefined-time bipartite consensus control for uncertain nonlinear multiagent systems under a signed directed topology. All agents have high-order uncertain nonlinear dynamic characteristics satisfying a time-varying Lipschitz growth condition, and their partial state information is not available for measurement. In this case, we put forward a novel output-feedback-based predefined-time leader-following bipartite consensus control strategy. A predefined-time compensator for each follower is firstly constructed with a time-varying gain by utilizing its relative output information. Then, a novel linear-like output feedback predefined-time distributed control protocol is developed for each follower by means of the compensator. By making two artful state transitions, the bipartite consensus problem is reduced to a stabilization one of nonlinear systems. By means of the Lyapunov stability theorem, we strictly prove that the designed controllers can ensure that all agents realize bipartite consensus in predefined time. Finally, two simulation examples are given to validate the viability of the developed theoretical algorithm.

This paper addresses the output feedback distributed containment control problem for a class of nonlinear stochastic multiagent systems under a fixed directed graph. Existing works usually design the containment control protocol using backstepping design method based on a conservative Lipschitz condition on nonlinear functions, which has a tedious control design procedure. In this paper, a new output feedback distributed containment control algorithm is proposed based on a novel dynamic compensator, which can not only simplify the control design procedure but also relax the condition on nonlinear terms. The proposed distributed containment protocol for each agent depends only on the agent output and the relative outputs of its neighbor agents, and can reduce the communication burden between the agents. Based on the Lyapunov stability theory, it is proved that the outputs of the followers are driven into the convex hull spanned by the outputs of the leaders with the proposed linear controller. Finally, the effectiveness of the theoretical results is illustrated by simulation examples.

This article studies the finite-time observer-based leader-following consensus problem for a class of nonlinear multiagent systems with nonuniform time-varying input delays. Existing works usually assume that input delays are the same constants and the input of the leader is available for each follower. In this article, we propose a new distributed consensus algorithm to relax the conservative condition. A novel finite-time distributed observer is designed for each follower, which can accurately estimate the state information of the leader in a setting time. By means of the observer, the distributed controller is proposed for each follower, and it depends only on the state information of the follower and the estimated-state information of its neighbor agents. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus. Finally, the effectiveness of the theoretical results is verified by numerical simulation on a group of single-link manipulators.

This paper studies the problem of decentralized adaptive output feedback fault detection and control for a class of uncertain nonlinear interconnected systems. The K-filters are designed to estimate the unmeasured state variables of the system. Moreover, the built-in noise dampening filters are introduced to attenuate the influence caused by the measurement noises. Then the fault detection scheme is proposed by designing the residual and threshold signals. Subsequently, by using the backstepping design method, the decentralized switched control strategies are proposed with the help of the neural network approximation technique. Based on the Lyapunov stability theory, it is proved strictly that all signals of the resulting closed-loop system are bounded. Finally, a simulation example is presented to verify the effectiveness of the theoretical result.

This paper focuses on the problem of decentralized event-based dynamic output feedback adaptive fuzzy control for a class of interconnected stochastic nonlinear systems. In order to relax the Lipschitz condition for the nonlinearity, a novel dynamic gain observer is constructed to estimate the unmeasured state variables. The funnel-like control technique is proposed to ensure that the output of each subsystem satisfies the prescribed performance requirement. To save energy in signal transmission, the controller and its triggered mechanism are codesigned based on backstepping method. By using the approximation theory of fuzzy logic systems, an unknown continuous function is approximated, and the difficulty caused by unmodeled dynamics is removed with the aid of changing supply function idea. By applying the Lyapunov stability theory, it is proved that all the signals of the resulting closed-loop system with the designed controller are bounded in probability. Finally, simulation results are given to verify the effectiveness of the theoretical results.