In this paper, the practically predefined-time (PPT) consensus control problem is explored for nonlinear multiagent systems (MASs) affected by external disturbances and unknown control gains. First, a predefined-time disturbance observer is designed to eliminate the effects of lumped disturbance, ensuring that the rapid convergence of the lumped disturbance estimation error within predefined time. Second, a predefined-time dynamic surface filter is proposed to alleviate the issue of “explosion of complexity”. Then, the predefined-time controller is developed by combining smooth function to avoid singularity problem. By employing Lyapunov stability analysis, it is proven that the PPT stability of MASs can be ensured, which consensus tracking error can converge to a small region near zero within predefined time. The simulations further validate the efficacy and superiority of the proposed predefined-time control algorithm.

This article focuses on the secure predefined-time sliding mode control problem for a third-order heterogeneous vehicle platoon. For the purpose of reducing the effect due to the sensor measurement deviation and relaxing the system conservatism, a state estimation algorithm is designed based on the historical data and threshold judgment. Furthermore, a rate-coded reversible privacy-preserving mechanism with dual encryption is proposed and applied to vehicle-to-vehicle communications, which can guarantee the protection of the critical system data and the realizability of the desired predefined-time convergence performance by utilizing the origin outputs. In order to avoid the singularity problem and enhance the resistance of the vehicle platoon to the external disturbance, a nonsingular sliding mode surface and a corresponding predefined-time controller are designed. Based on the predefined-time stability and the string stability theorems, the vehicle platoon can be proved to be a practical predefined-time stable (PPTS) and string stable. Finally, adequate validations of the third-order heterogeneous vehicle platoon demonstrate the fast convergence speed and good robustness of the proposed control scheme.

This paper introduces a swift solution for the complementary coverage path planning issue for multiple uncrewed aerial vehicles (UAV) in 3-dimensional, non-convex, and trap-laden indoor environments. A topology graph is constructed by sampling the blueprint. To divide the indoor environment into several convex areas and form tasks for the UAVs, the Louvain (fast unfolding) algorithm is utilized twice for space partitioning and task assigning. Finally, to ascertain the path for each UAV, a tiny-scale traveling salesman problem (TSP) is formed and solved. The simulation result reveals that the proposed strategy has significantly improved efficiency in both 2-dimensional and 3-dimensional path planning, underscoring the algorithm’s practical relevance for high-rise fire rescue operations. Note to Practitioners—This paper is motivated by the requirement of urgent task assignment and route finding for multiple uncrewed aerial vehicles (UAV) in indoor searching, especially for high-rise fire rescue. A rapid task allocation and pathfinding framework that balances efficiency and optimization levels is proposed. Unlike other complex environments, community-detection-based task allocation methods are recommended for actual indoor space rather than clustering-based ones. Additionally, the degree of each node in the sampling graph can be used to rapidly determine the sensor’s detection range at that node and the distance to obstacles. Making reasonable use of this can further enhance the efficiency of task allocation and pathfinding. Extensive experimental results in actual indoor maps show the effectiveness of the proposed task allocation and pathfinding framework. Note that our method is designed only for indoor environments divided into multiple rooms by continuous walls, and the effectiveness in other types of environments cannot be guaranteed.

The situation of interest is where a vehicle is equipped with multiple sensors to measure the distance to the leading vehicle but does not need to obtain data from speed and acceleration sensors. The distance measurements are susceptible to asynchronous sampling and noise, nearly half of which may be manipulated by malicious attackers. In this situation, the event-triggered vehicle-following control problem of nonlinear autonomous vehicles with unknown parameters is studied. First, a secure event-triggered mechanism that can resist manipulation is devised to alleviate the burden of data transmission and processing caused by multiple sensors. Then, a novel adaptive sensor fusion algorithm is developed to estimate the actual distance. Subsequently, an improved adaptive observer is designed based on the event-triggered estimated distance to estimate continuous-time distance, velocity, acceleration, and system parameters. Finally, the following controller is designed using the estimated states and parameters with the help of Levant differentiators. The effectiveness of the proposed control scheme is validated through simulation studies.Note to Practitioners—This work aims to develop a secure following control method for nonlinear automated vehicles with unknown states and parameters, which can effectively handle sparse sensor problems caused by attacks, faults, saturation, etc. To address practical issues such as sampling intervals and limited computing and transmission resources, we propose a discrete sampling–event-triggered transmission–continuous estimation and control framework for continuous-time systems. Despite the presence of measurement interferences and potential corruption, the designed event-triggered mechanism and sensor fusion algorithm can be used to reduce data transmission and estimate the actual output, respectively. The designed adaptive observer can estimate continuous-time system states and parameters whether the output is obtained in a continuous, short-interval discrete or suitable event-triggered manner. This capability facilitates control design and real-time monitoring of vehicle states. Additionally, the presented backstepping control design and analysis method utilizing Levant differentiators can be applied in situations where the controlled system’s states possess at least first-order differentiability.

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. Note to Practitioners—This paper aims to develop a fully distributed MFASMC method to address the consensus problem for unknown MASs with hybrid attacks in network topologies. A hybrid attack compensation mechanism is proposed to mitigate the effects of neighbor information dropouts caused by hybrid attacks. By combining the sliding mode control theory with the model-free adaptive control strategy, the system’s robustness is improved to relieve the impact of neighbor information inaccuracies attributed to hybrid attacks. Since the proposed algorithm does not depend on the mathematical model and Laplace matrix of systems, it can be applied to large-scale MASs with unknown models to solve network topology security problems, such as multiple subway trains, wireless communication systems, and microgrid systems. Furthermore, the proposed method is simple in design, widely adaptable, and robust, making it easier to apply to practical systems and more friendly to control engineers.

This paper investigates the challenge of achieving event-triggered optimal consensus control for multiagent systems (MASs) with multiple constraints, encompassing saturation constraint at the input and performance constraint at the output. To achieve performance constraint while satisfying input saturation, a flexible prescribed performance method (FPPM) is designed. Utilizing non-negative signals generated by the improved auxiliary system to design the performance functions, the FPPM can adaptively adjust the performance constraint boundaries to ensure safe operation of the MASs with multiple constraints. Meanwhile, the proposed FPPM can achieve different performance behaviors by changing core parameters without the need to alter the control structure. Subsequently, a simplified reinforcement learning algorithm with actor-critic structure is integrated into the FPPM. By designing actor-critic neural networks and dynamic event-triggered mechanism, optimal consensus control for MASs under multiple constraint conditions is achieved cleverly while avoiding unnecessary communication transmissions. Finally, a simulation example verifies the effectiveness of the proposed method. Note to Practitioners—Considering the limitations of physical devices and the practical requirements for control performance, the input saturation constraint and performance constraint often coexist during the operation of practical systems, such as robotic systems, manipulator systems and aerospace systems. Therefore, this paper aims to design an event-triggered reinforcement learning algorithm for MASs with multiple constraints. To resolve the conflict problem caused by input saturation and performance constraint, a FPPM with adjustable performance functions is proposed. By flexibly adjusting the performance constraint boundaries, the coexistence problem of multiple constraints can be solved effectively. Meanwhile, the constructed FPPM framework can achieve various performance behaviors by adjusting parameters according to the practical application scenario without changing the controller structure. Additionally, the proposed event-triggered reinforcement learning algorithm can optimize the designed cost function and promote the utilization of communication resources.

In this article, we concentrate on the adaptive fuzzy containment control approach for a class of nonlinear multiagent systems with deferred constraint and actuator failure. First, considering that not all agents can directly receive the leader signals, this article constructs a distributed prescribed-time estimator to provide each agent with a corresponding reference signal, thereby the containment problem is constructed as a tracking problem. Subsequently, with the help of the prescribed-time scaling function and the barrier function, the problem of deferred output constraint is reformulated as a boundedness problem of the new variable. By introducing several useful lemmas, the designed controller can ensure that the closed-loop signals are bounded in the presence of actuator fault in the system. In addition, through the designed fuzzy control algorithm, it can strictly guarantee that the system output converges within the ideal range after the settling time. The superiority and effectiveness of this method are verified through robots experiments.

In this paper, the tracking control problem is studied in the model-free adaptive control (MFAC) framework for a class of discrete-time single-input single-output nonlinear systems affected by dynamic sparse attacks and measurement disturbances. The system outputs are measured by multiple sensors, but an attacker can manipulate nearly half of the sensors simultaneously in a time-varying manner. First, considering the communication burden caused by multiple sensors, a voting-based event-triggered mechanism is introduced to minimize data transmission under attacks. The triggering condition is designed according to tracking performance so that the system is updated only at the triggering instants while maintaining satisfactory control performance. Then, to minimize the effects of measurement disturbances and dynamic sparse attacks on the control performance of the MFAC algorithm, two data fusion algorithms are developed to estimate the system output from the transmitted data. Moreover, an event-triggered extended state observer is designed to mitigate the negative impact of nonlinear residual terms caused by estimation errors on the MFAC algorithm, and based on this, a controller that updates only at the triggering instants is designed. Finally, simulation examples confirm the effectiveness of the proposed MFAC algorithm.

In this article, the fuzzy dynamic event-triggered (DET) containment control problem for human-in-the-loop (HiTL) multiagent systems (MASs) with error constraints is investigated. Through utilization of fuzzy logic systems (FLSs), a high-gain state observer is presented to estimate unavailable states. To improve the transient performance, a fixed-time prescribed performance (FTPP) function is presented to restrict the containment errors and virtual errors. Under the backstepping control framework, a barrier Lyapunov function is designed to guarantee that the transform errors do not transgress the constraint bounds. Meanwhile, a nonlinear filter is introduced to obtain reduction calculation and enhance the control performance. Furthermore, the DET mechanism is presented to decrease the network communication burden. By directly controlling multiple leaders to form a dynamic convex hull, the proposed control strategy ensures that output signals of followers can converge to this convex hull and the containment errors can converge to the prescribed bounds within fixed time. Herein, a simulation example is presented to assess the effectiveness of the proposed control scheme.

In this article, an adaptive optimal consensus control problem is studied for multiagent systems (MASs) with external disturbances, unmeasurable states, and prescribed constraints. First, by using neural networks (NNs), a composite observer is constructed to estimate the unmeasurable states and disturbances simultaneously. Then, the consensus error is guaranteed within a prescribed boundary by presenting an improved prescribed performance control (PPC) technique, and the initial conditions for the error are eliminated. In addition, the updating laws of actor-critic NNs are established by using a simplified reinforcement learning (RL) algorithm based on the uniqueness of optimal solution, and the asymmetric input saturation is resolved by designing auxiliary system instead of using nonquadratic cost functions in other optimal control methods. Finally, the boundedness of all signals in the closed-loop system is proved by using Lyapunov stability theory. The effectiveness of the proposed control method is verified by a simulation example.

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Most existing approximation-based adaptive control (AAC) approaches for unknown pure-feedback nonaffine systems retain a dilemma that all closed-loop signals are semiglobally uniformly bounded (SGUB) rather than globally uniformly bounded (GUB). To achieve the GUB stability result, this article presents a neuro-adaptive backstepping control approach by blending the mean value theorem (MVT), the barrier Lyapunov functions (BLFs), and the technique of neural approximation. Specifically, we first resort the MVT to acquire the intermediate and actual control inputs from the nonaffine structures directly. Then, neural networks (NNs) are adopted to approximate the unknown nonlinear functions, in which the compact sets for maintaining the approximation capabilities of NNs are predetermined actively through the BLFs. It is shown that, with the developed neuro-adaptive control scheme, global stability of the resulting closed-loop system is ensured. Simulations are conducted to verify and clarify the developed approach.

In the fault-tolerant control (FTC) tasks of nonstrict-feedback nonlinear systems, unmeasurable states, disturbance, and actuator faults are recognized as the main factors that obstacle the effective controller design and, thus, the tracking performance improvement. To tackle these obstructions, a fuzzy observer is introduced to address the difficulties of the unmeasurable states involving nonstrict-feedback nonlinear systems by benefiting from the approximation property of fuzzy logic systems. Owing to the newly employed damping term in the intermediate control law being utilized to compensate for the possibly unlimited number of faults, the proposed FTC strategy is able to deal with actuator faults properly without imposing tighter requirements on the fault mechanism. To reach fast transient performance, stability of finite time is reached by exploiting the backstepping method. The investigated strategy ensures that all the responses of the systems are semiglobal practical finite-time stable. Meanwhile, the tracking error converges to a small neighborhood of the origin within finite time. In addition, to demonstrate its effectiveness, the provided approach is applied to the position tracking of a robotic system, which shows anticipated control performances in spite of various uncertainties.

This paper investigates the secure consensus tracking problem for continuous-time nonlinear multiagent systems with sensor attacks. By designing a secure data selector, the unattacked output data is extracted from a group of output measurements under sparse sensor attacks. Then, by virtue of the obtained data and neural networks, a state observer is constructed to estimate the unavailable system states, where the convex combination theory is introduced to reduce the difficulty of solving observation gains. To utilize the limited communication resources reasonably, a novel saturated threshold event-triggered control strategy is proposed to reduce control updates, and then each update is encoded into a binary signal (0 or 1) to further reduce the occupation of communication bandwidth. The designed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded, and its effectiveness is verified via a simulation of attitude control of unmanned aerial vehicles.

This paper considers the distributed containment control problem for human-in-the-loop (HiTL) multiagent systems (MASs) subject to unknown time-varying parameters and input saturation. A smooth function containing positive integrable time-varying function is embedded in the controller to compensate for the negative effects of unknown time-varying parameters and uncertain disturbances. Meanwhile, an auxiliary system with the same order as the considered system is skillfully introduced into the backstepping control method to overcome the problem of input saturation. By constructing an adaptive command filter with error compensation mechanism, the problems of the computation burden and filtering errors are solved simultaneously. Moreover, the output signals of followers can converge into the convex hull spanned by multiple dynamic leaders which are controlled by a human operator. Based on the Lyapunov stability theory, it is shown that the containment errors can asymptotically converge into the prescribed bounds. Finally, two simulation examples evaluate the effectiveness of the presented control scheme.

In this paper, the distributed optimal containment control problem for multiple nonholonomic mobile robots (NHMRs) differential game is studied via reinforcement learning. An approximation-based optimal control strategy is developed to ensure the optimal performance index and avoid the potential collision among agents. Firstly, the collision avoidance problem considered in this paper is addressed by exploiting a consensus-like interconnection on a directed graph and an error transformation function. Then, on the basis of the optimal backstepping technique, a single critic neural network is adopted to obtain the solution of the coupled Hamilton-Jacobi (HJ) equation, in which an improved learning mechanism is constructed to relax the requirement on initial control conditions. In addition, based on the Lyapunov stability theory, it is proved that all signals in the closed-loop optimal control are uniformly ultimately bounded. Finally, the proposed control protocol is applied to NHMRs system, which verifies that the solution of the coupled HJ equation solves the containment problem of differential game.

This article studies the bipartite tracking control problem of distributed nonlinear multiagent systems with input quantization, external disturbances, and actuator faults. We use the radial basis function (RBF) neural networks (NNs) to model unknown nonlinearities. Due to the fact that the upper bounds of disturbances and the number of actuator faults are unknown, an intermediate control law is designed based on a backstepping strategy, where a compensation term is introduced to eliminate external disturbances and actuator faults. Meanwhile, a novel smooth function is incorporated into the real distributed controller to reduce the effect of quantization on the virtual controller. The proposed distributed controller not only realizes the bipartite tracking control but also ensures that all signals are bounded in the closed-loop systems and the outputs of all followers converge to a neighborhood of the leader output. Finally, simulation results demonstrate the effectiveness of the proposed control algorithm.

In this article, the problem of adaptive tracking control is tackled for a class of high-order nonlinear systems. In contrast to existing results, the considered system contains not only unknown nonlinear functions but also unknown rational powers. By utilizing the fuzzy approximation approach together with the barrier Lyapunov functions (BLFs), we present a new adaptive tracking control strategy. Remarkably, the BLFs are employed to determine a priori the compact set for maintaining the validity of fuzzy approximation. The primary advantage of this article is that the developed controller is independent of the powers and can be capable of ensuring global stability. Finally, two illustrative examples are given to verify the effectiveness of the theoretical findings.

An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator (SFRM) with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system. By designing a performance function, the transient performance of the control system is guaranteed. To stabilize the SFRM, a dynamic signal is applied to handle the unmodeled dynamics. To cut down the communication load of the channel, the event-triggered control law is developed based on the switching threshold strategy. The Lyapunov stability theory and backstepping technique are applied coordinately to design the control strategy. The semiglobally ultimately uniformly boundedness can be ensured for all signals in the closed-loop system. The designed control method can also guarantee that the tracking error can converge to a small neighborhood of zero within the prescribed performance boundaries. At the end of the article, two illustrative examples are shown to validate the designed event-triggered controller.

This article proposes a finite-time adaptive containment control scheme for a class of uncertain nonlinear multiagent systems subject to mismatched disturbances and actuator failures. The dynamic surface control technique and adding a power integrator technique are modified to develop the distributed finite-time adaptive containment algorithm, which shows lower computational complexity. In order to overcome the difficulty from the mismatched uncertainties, the disturbance observers are constructed based on the backstepping technique. Moreover, the uncertain actuator faults, including loss of effectiveness model and lock-in-place model, are considered and compensated by the proposed adaptive control scheme in this article. According to the Lyapunov stability theory, it is demonstrated that the containment errors are practically finite-time stable in the presence of actuator faults. Finally, a simulation example is conducted to show the effectiveness of the proposed theoretical results.

This article considers the Nussbaum gain adaptive control issue for a type of nonlinear systems, in which some sophisticated and challenging problems, such as periodic disturbances, dead zone output, and unknown control direction are addressed. The Fourier series expansion and radial basis function neural network are incorporated into a function approximator to model time-varying-disturbed function with a known period in nonlinear systems. To deal with the problems of the dead zone output and unknown control direction, the Nussbaum-type function is recommended in the design of the control algorithm. Applying the Lyapunov stability theory and backstepping technique, the proposed control strategy ensures that the tracking error is pulled back to a small neighborhood of origin and all closed-loop signals are bounded. Finally, simulation results are presented to show the availability and validity of the analysis approach.

This article investigates the event-triggered containment control problem for stochastic nonlinear multiagent systems with unknown Bouc–Wen hysteresis input. Based on the backstepping technique, an adaptive fuzzy event-triggered containment control scheme is proposed, which is conditionally updated only at the sampled instants. Fuzzy logic systems are used to approximate the unknown nonlinear functions. In addition, a Nussbaum function is utilized to eliminate the effect of unknown hysteresis. Moreover, an event-triggered mechanism is introduced to reduce the communication burden. By using stochastic Lyapunov stability theory and graph theory, it is proved that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Meanwhile, all the followers’ outputs converge to the dynamic convex hull spanned by the dynamic leaders. Finally, the effectiveness of the proposed control scheme is illustrated by some simulation results.

This article studies the bipartite containment control problem for nonlinear multiagent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol such that the followers converge to a convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition approach of input quantization, an event-triggered control scheme is developed via backstepping technique. A fuzzy observer is constructed to estimate unmeasurable states. Moreover, the bipartite containment control scheme for nonlinear MASs is designed. It is demonstrated that all signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior is excluded. Finally, a simulation example is given to verify the validity of the designed method.

This article investigates the event-triggered control problem for stochastic nonlinear systems with unmeasured states and unknown backlash-like hysteresis. Based on the fuzzy logic systems, the unknown nonlinear functions can be identified. Then, by utilizing a fuzzy state observer, the unmeasured states of the considered system can be estimated. Moreover, by introducing an event-triggered mechanism, the communication load can be largely reduced. By employing the backstepping control strategy and the adaptive control method, a novel adaptive fuzzy event-triggered control method is constructed. It is shown that whole signals in the closed-loop systems are, ultimately, semiglobally and uniformly bounded in probability. Moreover, the tracking errors and the observer errors are located in a small neighborhood around the origin. Finally, a numerical example is given to confirm the effectiveness of the design scheme.

An observer-based adaptive dynamic surface control (DSC) strategy is proposed for nonlinear nonstrict-feedback systems with time-varying disturbance, event-triggered mechanism, and actuator failures in this paper. Fuzzy logic systems are implemented to construct an observer to estimate the unmeasured states. The DSC method is exploited to solve the issue of “explosion of complexity” with the backstepping control technique. The constructed event-triggered mechanism can avoid the waste of communication resources. The barrier Lyapunov functions are utilized to address the problem of output constraint. It is demonstrated that the reference signals can be well tracked by the system output and all closed-loop signals remain semi-globally uniformly ultimately bounded. Two examples illustrate the effectiveness of the constructed controller.

This article considers the funnel output tracking control for a class of high-order uncertain nonlinear systems with the powers of positive odd rational numbers, aiming to accomplish output tracking with prescribed accuracy when both the system nonlinearities and the powers of the system are unknown. For such a purpose, a robust funnel control algorithm, i.e., a continuous, static, and universal, state-feedback controller is explicitly constructed, which achieves the state errors evolving within the predesigned performance space. Benefits of the proposed funnel output tracking controller comparing with the current approaches lie in the fact that the exact knowledge of system nonlinearities, including generally required bounding functions, is not needed to be a priori. Moreover, all the powers in each high-order subsystem are permitted to be any unknown positive odd rational numbers as well. The efficacy of the developed algorithm is confirmed through two illustrative examples.

This article addresses the problem of decentralized adaptive fuzzy fixed-time control for a class of pure-feedback interconnected nonlinear systems with full-state tracking error constraints. The fuzzy logic systems (FLSs) are adopted to model the unknown nonlinear functions. Combining the properties of the prescribed performance functions (PPFs) with barrier Lyapunov functions (BLFs), the predefined state tracking error dynamic performance and good tracking accuracy are guaranteed. Then, on the basis of the backstepping recursive design technique, a structurally simple adaptive fuzzy fixed-time controller is designed. Furthermore, by the fixed-time stability criterion and the Lyapunov stability theory, it is proven that the boundedness of system signals is guaranteed, and the full-state tracking errors can evolve within the prescribed performance regions within the fixed time. Finally, the effectiveness of the proposed control strategy is demonstrated by some simulation results.

In this paper, an adaptive neural network (NN) control problem is investigated for discrete-time nonlinear systems with input saturation. Radial-basis-function (RBF) NNs, including critic NNs and action NNs, are employed to approximate the utility functions and system uncertainties, respectively. In the previous works, a gradient descent scheme is applied to update weight vectors, which may lead to local optimal problem. To circumvent this problem, a multigradient recursive (MGR) reinforcement learning scheme is proposed, which utilizes both the current gradient and the past gradients. As a consequence, the MGR scheme not only eliminates the local optimal problem but also guarantees faster convergence rate than the gradient descent scheme. Moreover, the constraint of actuator input saturation is considered. The closed-loop system stability is developed by using the Lyapunov stability theory, and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed approach is further validated via some simulation results.

In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed H∞ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

The problem of finite-horizon H∞state estimator design for periodic neural networks over multiple fading channels is studied in this paper. To characterize the measurement signals transmitted through different channels experiencing channel fading, a multiple fading channels model is considered. For investigating the situation of correlated fading channels, a set of correlated random variables is introduced. Specifically, the channel coefficients are described by white noise processes and are assumed to be correlated. Two sufficient criteria are provided, by utilizing a stochastic analysis approach, to guarantee that the estimation error system is stochastically stable and achieves the prescribed H∞performance. Then, the parameters of the estimator are derived by solving recursive linear matrix inequalities. Finally, some simulation results are shown to illustrate the effectiveness of the proposed method.

For a class of large-scale nonlinear systems in nonstrict-feedback structure with immeasurable states, an adaptive decentralized fuzzy control strategy on the basis of event-triggered mechanism is investigated in this paper. Fuzzy logic systems are implemented to construct an observer, which approximates the unknown nonlinear function in the controller. In light of backstepping control technique and event-triggered mechanism, a decentralized adaptive fuzzy control approach is proposed to compensate for the effects of actuator faults. When the triggering condition is satisfied, the communication burden can be reduced. Moreover, the whole signals of the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded. Furthermore, the outputs of subsystems can track the desired reference signals. Finally, some simulation results are utilized to testify the effectiveness of the proposed control scheme.

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness.

This paper addresses the containment control problem of nonidentical networks with external disturbance via adaptive distributed observer method. We first present a formulation for containment error, which ensures all the followers' outputs converge to the convex hull spanned by the leaders' reference outputs. Then by using an adaptive distributed observer, the leaders' system matrices and the states of convex hull are both estimated. Furthermore, a novel global regulate equation is designed to restrain the external disturbance, and the problem of containment control for multiagent systems is solved by a dynamic output feedback approach. Finally, some simulation results are given to illustrate the validity of the theoretical results.

This paper investigates the adaptive tracking control problem for a class of uncertain single-input and single-output strict-feedback nonlinear systems with unknown control direction and disturbances. Dynamic surface control is utilized to handle the problem of “explosion of complexity” occurred in the conventional backstepping design. In order to escape analytic calculation, a first-order filter is used to generate the command signals and their derivatives. Moreover, Nussbaum function is employed to handle the problem of the unknown control coefficient. New controllers and adaptive laws are designed by combining the compensation tracking error and the prediction error that exist between the system state and the serial-parallel estimation model. It is proved that all the variables in the closedloop system are bounded and the tracking error is driven to the origin with a small neighborhood. Finally, the simulation results are presented to verify the effectiveness of the proposed approach.

This paper is focused on the observer-based adaptive fuzzy control problem for nonlinear stochastic systems with the nonstrict-feedback form, in which some complicated and challenging issues including unmeasurable states, input quantization and actuator faults are addressed. The fuzzy logic systems are introduced to approximate the nonlinear functions existing in the control system. A fuzzy observer is designed to observe the unavailable state variables. In order to handle the negative effects resulting from input quantization and actuator faults, a damping term with the estimation of unknown bounds as well as a positive time-varying integral function are constructed, respectively. Furthermore, an observer-based adaptive fuzzy control scheme is proposed for the considered systems to compensate for the effects of input quantization and actuator fault based on adaptive back-stepping approach. The proposed control strategy can guarantee that all the signals in the closed-loop system are bounded. Finally, simulation results are provided to illustrate the effectiveness of the proposed adaptive control scheme.

This paper investigates the problem of adaptive fuzzy control on the basis of an event-triggered mechanism for nonlinear strict-feedback systems with time-varying external disturbances and virtual control coefficients in the presence of actuator failures. Virtual control coefficients are correlated with the designed adaptive law and control signal. In the backstepping technique procedure, fuzzy logic systems are utilized to approximate an unknown nonlinear function, and the tuning function is implemented to cope with the destabilizing problem of the control design. To save communication resources, an adaptive fuzzy event-triggered control strategy is developed to update the control input when the triggering condition is satisfied. Then, all of the closed-loop signals can remain semi-globally uniformly ultimately bounded. The Zeno behavior can be excluded. Finally, a numerical example and a real system are provided to illustrate the effectiveness of the proposed approach.

This paper is focused on the issue of scheduling stabilization of Takagi-Sugeno fuzzy control systems by the aid of digging down much deeper of implicit information in the underlying systems. An event-triggered real-time scheduler that decides which control mode should be executed at any given instant is constructed by periodically evaluating the joint-distribution-type of multi-instant normalized fuzzy weighting functions at every sampled instant. Profiting from the proposed event-triggered scheduling policy, the proper control mode for the current instant is updated in order to adapt time-varying situations once if the underlying joint-distribution-type changes, and thus previous implementations of control tasks with an unchanged control mode can be further relaxed in this paper. The effectiveness of our approach is verified by several simulation examples in the end.

This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the proposed controller is able to ensure the semi-global boundedness of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. The main advantage about this research is that a neural networks-based tracking control method is developed for uncertain nonlinear systems with unmodeled dynamics and nonlower triangular form. Simulation results demonstrate the feasibility of the newly presented design techniques.

An approximation-based adaptive neural controller is constructed for uncertain stochastic nonlinear systems in nonstrict-feedback form appearing dead-zone and output constraint. Neural networks (NNs) are directly utilized to approximate the unknown nonlinear functions existing in systems. A barrier Lyapunov function is introduced to ensure that the trajectory of output is limited within a predetermined range. By integrating NNs into the backstepping technique, an adaptive neural controller is designed to guarantee all variables existing in the considered closed-loop system are semi-globally uniformly ultimately bounded, and by appropriately tuning several design parameters online, the tracking error can be converged to a small neighborhood of the origin. Simulations on a numerical example are given to demonstrate the effectiveness of the method proposed in this paper.

This paper studies an adaptive fuzzy tracking control problem for nonlinear stochastic systems with input saturation and nonstrict-feedback form. We use fuzzy logic systems to approximate unknown nonlinear functions. A novel approach is introduced to tackle unknown functions with nonstrict-feedback structure in the design process. By introducing an auxiliary system, the input saturation problem can be solved. Moreover, based on backstepping control design approach, a novel adaptive fuzzy tracking controller is designed to guarantee all signals in the closed-loop system to be bounded, and the system output can be driven to track the trajectory of a given reference signal. Finally, some simulation results are given to confirm the effectiveness of the proposed approach.

This paper investigates the problem of adaptive fuzzy state-feedback control for a category of single-input and single-output nonlinear systems in nonstrict-feedback form. Unmodeled dynamics and input constraint are considered in the system. Fuzzy logic systems are employed to identify unknown nonlinear characteristics existing in systems. An appropriate Lyapunov function is chosen to ensure unmodeled dynamics to be input-to-state practically stable. A smooth function is introduced to tackle input saturation. In order to overcome the difficulty of controller design for nonstrict-feedback system in backstepping design process, a variables separation method is introduced. Moreover, based on small-gain technique, an adaptive fuzzy controller is designed to guarantee all the signals of the resulting closed-loop system to be bounded. Finally, two illustrative examples are given to validate the effectiveness of the new design techniques.

This paper presents an adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint. A variable separation approach is introduced to overcome the difficulty arising from the nonstrict-feedback structure. The problem of input saturation is solved by introducing an auxiliary design system, and output constraint is handled by utilizing a barrier Lyapunov function. Combing fuzzy logic system with the adaptive backstepping technique, the semi-global boundedness of all variables in the closed-loop systems is guaranteed, and the tracking error is driven to the origin with a small neighborhood. The stability of the closed-loop systems is proved, and the simulation results reveal the effectiveness of the proposed approach.

This paper is concerned with the adaptive sliding mode control problem of uncertain nonlinear systems. Interval type-2 Takagi-Sugeno (T-S) fuzzy model is employed to represent uncertain nonlinear systems. The input matrices of the nonlinear systems are allowed to be different for the sliding mode controller design. The uncertain parameters are described by the lower and upper membership functions. An integral sliding mode surface is designed for analysis of sliding motion. Based on the sliding mode surface, a novel sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Some simulation results are given to illustrate the effectiveness of the presented control scheme.

In this paper, an approximation-based adaptive tracking control approach is proposed for a class of multiinput multioutput nonlinear systems. Based on the method of neural network, a novel adaptive controller is designed via backstepping design process. Furthermore, by introducing Nussbaum function, the issue of unknown control directions is handled. In the backstepping design process, the dynamic surface control technique is employed to avoid differentiating certain nonlinear functions repeatedly. Moreover, in order to reduce the number of adaptation laws, we do not use the neural networks to directly approximate the unknown nonlinear functions but the desired control signals. Finally, we provide two examples to illustrate the effectiveness of the proposed approach.

This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index. Attention is focused on solving the H∞, L2-L∞, passive, and dissipativity fuzzy filter design problems for IT2 fuzzy systems with D stability constraints in a unified frame. Under the new performance index frame, using Lyapunov stability theory, a novel type of IT2 filter is designed such that the filtering error system guarantees the prescribed H∞, L2-L∞, passive, and dissipativity performance levels with D stability constraints. The existence condition of the IT2 filter is expressed as the convex optimization problem, and the filter parameters in the condition can be solved by the standard software. The IT2 fuzzy model and IT2 fuzzy filter do not need to share the same lower and upper membership functions. Finally, a numerical example is provided to show the effectiveness of the proposed results.

In this paper, a novel design method for adaptive tracking control is proposed for robot finger dynamics. First, the dynamics are described by considering the robot finger as a large-scale system since it has many joints and multi-degrees of freedoms (DOFs). Second, by employing the direct adaptive fuzzy approximation method to approximate the unknown and desired control input signals instead of the unknown nonlinear functions, the number of adaptive design parameters obtained in the control design process is greatly reduced. Moreover, it is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded. Finally, simulations are conducted on the Puma 560 robot manipulator, and the results show the effectiveness of the developed control design approach.

In this paper, the problem of adaptive fuzzy tracking control via output feedback for a class of uncertain single-input single-output (SISO) strict-feedback nonlinear systems with unknown time-delay functions is investigated. Dynamic surface control technique is used to avoid the problem of “explosion of complexity,” which is caused by repeated differentiation of certain nonlinear functions in the backstepping design process. In addition, the fuzzy logic systems are utilized to approximate the unknown and desired control input signals directly instead of the unknown nonlinear functions. The designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded and the tracking error to converge to a small neighborhood of the origin. Simulations results are provided to demonstrate the effectiveness of the proposed methods.

This paper considers the problem of observer-based adaptive neural network (NN) control for a class of single-input single-output strict-feedback nonlinear stochastic systems with unknown time delays. Dynamic surface control is used to avoid the so-called explosion of complexity in the backstepping design process. Radial basis function NNs are directly utilized to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. The proposed adaptive NN output feedback controller can guarantee all the signals in the closed-loop system to be mean square semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the proposed methods.

This paper focuses on the problem of neural-network-based decentralized adaptive output-feedback control for a class of nonlinear strict-feedback large-scale stochastic systems. The dynamic surface control technique is used to avoid the explosion of computational complexity in the backstepping design process. A novel direct adaptive neural network approximation method is proposed to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. It is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded in a mean square. Simulation results are provided to demonstrate the effectiveness of the developed control design approach.

This paper is concerned with the problem of adaptive fuzzy tracking control via output feedback for a class of uncertain single-input single-output (SISO) strict-feedback nonlinear systems. The dynamic feedback strategy begins with an input-driven filter. By utilizing fuzzy logic systems to approximate unknown and desired control input signals directly instead of the unknown nonlinear functions, an output-feedback fuzzy tracking controller is designed via a backstepping approach. It is shown that the proposed fuzzy adaptive output controller can guarantee that all the signals remain bounded and that the tracking error converges to a small neighborhood of the origin. Simulations results are presented to demonstrate the effectiveness of the proposed methods.

This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.