Showing papers on "Bounded function published in 2022"

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Abstract: This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.

Distributed Observer-Based Cooperative Control Approach for Uncertain Nonlinear MASs Under Event-Triggered Communication

Abstract: The distributed tracking problem for uncertain nonlinear multiagent systems (MASs) under event-triggered communication is an important issue. However, existing results provide solutions that can only ensure stability with bounded tracking errors, as asymptotic tracking is difficult to be achieved mainly due to the errors caused by event-triggering mechanisms and system uncertainties. In this article, with the aim of overcoming such difficulty, we propose a new methodology. The subsystems in MASs are divided into two groups, in which the first group consists of the subsystems that can access partial output of the reference system and the second one contains all the remaining subsystems. To estimate the state of the reference system, a new distributed event-triggered observer is first designed for the first group based on a combined output observable condition. Then, a distributed event-triggered observer is proposed for the second group by employing the observer state of the first group. Based on the designed observers, adaptive controllers are derived for all subsystems. It is established that global stability of the closed loop system is ensured and asymptotic convergence of all the tracking errors is achieved. Moreover, a simulation example is provided to show the effectiveness of the proposed method.

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Abstract: This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.

Fully Distributed Scaled Consensus Tracking of High-Order Multiagent Systems With Time Delays and Disturbances

Abstract: This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems Different from the existing results, for high-order nonlinear multiagent systems with time delays and external disturbances, a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios The control gains are varying and updated by distributed adaptive laws As a result, the presented protocol is independent of any global information, and thus, could be implemented in a fully distributed manner Simultaneously, the fully distributed control protocol using an adaptive $\sigma$ -modification technique is presented to deal with external disturbances, which can guarantee the tracking errors and coupling weights of all following agents are uniformly ultimately bounded To tackle with the derivatives of the functionals with time delays, the Lyapunov–Krasovskii functional is employed to analyze and compensate them by introducing multiintegral terms Finally, simulation examples are included to verify the effectiveness of the theoretical results

Fuzzy Adaptive Optimized Leader-Following Formation Control for Second-Order Stochastic Multiagent Systems

Abstract: In this article, an adaptive optimized formation control problem is studied for the second-order stochastic multiagent systems (MASs) with unknown nonlinear dynamics. Compared with first-order formation control, the second-order MASs consider not only the states but also the states rates, which is certainly more challenging and difficult work. In the control design of this article, the fuzzy logic systems are applied to approximate the nonlinear functions. By employing the actor-critic architecture and Lyapunov stability theory, the proposed optimal formation control strategy ensures that all the error signals are bounded in probability. Finally, the simulation examples verify that the proposed formation control approach achieves desired results.

Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance

Abstract: This article investigates the adaptive neural network fixed-time tracking control issue for a class of strict-feedback nonlinear systems with prescribed performance demands, in which the radial basis function neural networks (RBFNNs) are utilized to approximate the unknown items. First, an modified fractional-order command filtered backstepping (FOCFB) control technique is incorporated to address the issue of the iterative derivation and remove the impact of filtering errors, where a fractional-order filter is adopted to improve the filter performance. Furthermore, an event-driven-based fixed-time adaptive controller is constructed to reduce the communication burden while excluding the Zeno-behavior. Stability results prove that the designed controller not only guarantees all the signals of the closed-loop system (CLS) are practically fixed-time bounded, but also the tracking error can be regulated to the predefined boundary. Finally, the feasibility and superiority of the proposed control algorithm are verified by two simulation examples.

Fully Distributed Scaled Consensus Tracking of High-Order Multiagent Systems With Time Delays and Disturbances

Abstract: This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems. Different from the existing results, for high-order nonlinear multiagent systems with time delays and external disturbances, a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios. The control gains are varying and updated by distributed adaptive laws. As a result, the presented protocol is independent of any global information, and thus, could be implemented in a fully distributed manner. Simultaneously, the fully distributed control protocol using an adaptive $\sigma$ -modification technique is presented to deal with external disturbances, which can guarantee the tracking errors and coupling weights of all following agents are uniformly ultimately bounded. To tackle with the derivatives of the functionals with time delays, the Lyapunov–Krasovskii functional is employed to analyze and compensate them by introducing multiintegral terms. Finally, simulation examples are included to verify the effectiveness of the theoretical results.

Ultimately Bounded Filtering Subject to Impulsive Measurement Outliers

Abstract: This article is concerned with the ultimately bounded filtering problem for a class of linear time-delay systems subject to norm-bounded disturbances and impulsive measurement outliers (IMOs). The considered IMOs are modeled by a sequence of impulsive signals with certain known minimum norm (i.e., the minimum of the norms of all impulsive signals). In order to characterize the occasional occurrence of IMOs, a sequence of independent and identically distributed random variables is introduced to depict the interval lengths (i.e., the durations between two adjacent IMOs) of the outliers. In order to achieve satisfactory filtering performance, a novel parameter-dependent filtering approach is proposed to protect the filtering performance from IMOs by using a special outlier detection scheme, which is developed based on a particular input–output model. First, the ultimate boundedness (in mean square) of the filtering error is investigated by using the stochastic analysis technique and the Lyapunov-functional-like method. Then, the desired filter gain matrix is derived through solving a constrained optimization problem. Furthermore, the designed filtering scheme is applied to the case where the statistical properties about the interval lengths of outliers are completely unknown. Finally, a simulation example is provided to demonstrate the effectiveness of our proposed filtering strategy.

Dynamics of imperfect inhomogeneous nanoplate with exponentially-varying properties resting on viscoelastic foundation

Abstract: This article tries to investigate the dynamic deflection response of exponentially functionally graded material (E-FGM) nanoplate considering the role of porosities when embedded in a visco-elastic foundation and subjected to moving load, for the first time. The effective material properties are found using an exponential model of the rule of mixture. Next, the governing equations for the nanoplates while resting on a visco-Winkler foundation are found based on the third-order shear deformation theory in conjunction with Eringen nonlocal elasticity by developing Hamilton's principle. To solve the time-dependent governing motion equations, a state-space method is developed to find the response of the structure including simply-supported edges under moving load. Through some examples, the validation of the approach is provided before investigating the roles of nonlocality, volume fraction index (exponential parameter), porosity index, visco-elastic foundation coefficients, and velocity and time span of moving load on the forced vibration of embedded E-FGM nano-size plate under moving load.

Dynamic Event-triggered State Estimation for Nonlinear Coupled Output Complex Networks Subject to Innovation Constraints

Abstract: This letter investigates the recursive state estimation (RSE) problem for a class of coupled output complex networks via the dynamic event-triggered communication mechanism (DETCM) and innovation constraints (ICs). Firstly, a DETCM is employed to regulate the transmission sequences. Then, in order to improve the reliability of network communication, a saturation function is introduced to constrain the measurement outliers. A new RSE method is provided such that, for all output coupling, DETCM and ICs, an upper bound of state estimation error covariance (SEEC) is presented in a recursive form, whose trace can be minimized via parameterizing the state estimator gain matrix (SEGM). Moreover, the theoretical analysis is given to guarantee that the error dynamic is uniformly bounded. Finally, a simulation example is illustrated to show the effectiveness of the proposed RSE method.

Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

Abstract: An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections, unknown time-varying parameters, and disturbances. First, by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time, all extra signals in the framework of decentralized control are filtered out, thereby removing all additional assumptions imposed on the interconnections, such as upper bounding functions and matching conditions. Second, by introducing two integral bounded functions, asymptotic tracking control is realized. Moreover, the nonlinear filters with the compensation terms are introduced to circumvent the issue of “explosion of complexity”. It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero asymptotically. In the end, a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

Adaptive Fuzzy Tracking Control of Switched MIMO Nonlinear Systems With Full State Constraints and Unknown Control Directions

Abstract: In this brief, an adaptive fuzzy tracking control method is proposed for switched multi-input multi-output (MIMO) nonlinear systems with time-varying full state constrains (TFSCs) and unknown control directions. First, the fuzzy logic systems are utilized to approximate unknown dynamic functions. A tangent barrier Lyapunov function (BLF-Tan) is used to solve the problem of TFSCs, and the unknown control directions problem is addressed by applying Nussbaum-type function. Then, an adaptive tracking controller is constructed by the backstepping technique. Under the designed control scheme, all the systems signals are derived to be bounded, and the tracking error of the systems is converged to a neighborhood near zero. Finally, the simulation example illustrates the control design programme is reasonable and effective.

Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

Abstract: An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections, unknown time-varying parameters, and disturbances. First, by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time, all extra signals in the framework of decentralized control are filtered out, thereby removing all additional assumptions imposed on the interconnections, such as upper bounding functions and matching conditions. Second, by introducing two integral bounded functions, asymptotic tracking control is realized. Moreover, the nonlinear filters with the compensation terms are introduced to circumvent the issue of “explosion of complexity”. It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero asymptotically. In the end, a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems

Abstract: This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It is assumed that the six-rotor UAV systems are controlled by a human operator sending command signals to the leader. A disturbance observer and radial basis function neural networks (RBF NNs) are applied to address the problems regarding external disturbances and uncertain nonlinear dynamics, respectively. In addition, the proposed finite-time command filtered (FTCF) backstepping method effectively manages the issue of ``explosion of complexity,'' where filtering errors are eliminated by the error compensation mechanism. In addition, an event-triggered mechanism is considered to alleviate the communication burden between the controller and the actuator in practice. It is shown that all signals of the six-rotor UAV systems are bounded and the consensus errors converge to a small neighborhood of the origin in finite time. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

Boundary Feedback Control of a Nonhomogeneous Wind Turbine Tower With Exogenous Disturbances

Abstract: This article studies a vibration and disturbance rejection problem of a wind turbine tower under exogenous perturbations. The tower dynamics is captured by a nonhomogeneous Euler–Bernoulli beam model. The dissipativity of the system is realized by a boundary feedback control solution with a multivalued symbolic function. A Lyapunov-based stability analysis is established to assess the deflection of the tower is uniformly bounded even subject to exogenous disturbances. The extended Filippov framework and Galerkin approximation scheme are introduced to tackle the existence of the solution to the system with a discontinuous control input. Simulation results demonstrate the performance of the proposed control scheme.

Observer-Based Adaptive Optimized Control for Stochastic Nonlinear Systems With Input and State Constraints

Abstract: In this work, an adaptive neural network (NN) optimized output-feedback control problem is studied for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. A nonlinear state observer is designed to estimate the unmeasured states, and the NNs are used to approximate the unknown nonlinear functions. Under the framework of the backstepping technique, the virtual and actual optimal controllers are developed by employing the actor–critic architecture. Meanwhile, the tan-type Barrier optimal performance index functions are developed to prevent the nonlinear systems from the state constraints, and all the states are confined within the preselected compact sets all the time. It is worth mentioning that the proposed optimized control is clearly simple since the reinforcement learning (RL) algorithm is derived based on the negative gradient of a simple positive function. Furthermore, the proposed optimal control strategy ensures that all the signals in the closed-loop system are bounded. Finally, a practical simulation example is carried out to further illustrate the effectiveness of the proposed optimal control method.

Adaptive Fuzzy Event-Triggered Command-Filtered Control for Nonlinear Time-Delay Systems

Abstract: This article focuses on an adaptive fuzzy dynamic event-triggered tracking control for nonlinear time-delay systems with unmodeled dynamics via a command filter method. Fuzzy logic systems are utilized to address the unknown nonlinear functions. The upper bound of the approximation error is allowed to be unknown and can be compensated by skillfully introducing a hyperbolic tangent function to the design of the adaptive laws. Meanwhile, without requiring any assumptions, time delays can be handled by appropriately incorporating the delayed nonlinear functions into the Lyapunov–Krasovskii functional. Then, a dynamic event-triggered control mechanism is designed to dynamically adjust the threshold parameter. Finally, a new adaptive controller is constructed such that all states of the closed-loop system are bounded. The system output is demonstrated to follow the desired signal. Two examples are given to illustrate the validity of the presented method.

Approximation-Based Nussbaum Gain Adaptive Control of Nonlinear Systems With Periodic Disturbances

Abstract: 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.

Predefined-Time Predefined-Bounded Attitude Tracking Control for Rigid Spacecraft

Abstract: In this article, we consider the attitude tracking control problem for rigid spacecraft with bounded external disturbances. We propose a predefined-time predefined-bounded attitude tracking control scheme based on a nonsingular predefined-time sliding-mode manifold. The proposed controller is continuous and it can achieve predefined-time predefined-bounded stability. That is, the attitude tracking errors are driven to a predefined-bounded region around the origin within a predefined time, which can be set as a tuning parameter during the controller design, independently of initial conditions. Finally, numerical simulations are carried out to evaluate the performance of the proposed control law.

Adaptive NN-Based Consensus for a Class of Nonlinear Multiagent Systems With Actuator Faults and Faulty Networks

Abstract: This article addresses the problem of fault-tolerant consensus control of a general nonlinear multiagent system subject to actuator faults and disturbed and faulty networks. By using neural network (NN) and adaptive control techniques, estimations of unknown state-dependent boundaries of nonlinear dynamics and actuator faults, which can reflect the worst impacts on the system, are first developed. A novel NN-based adaptive observer is designed for the observation of faulty transformation signals in networks. On the basis of the NN-based observer and adaptive control strategies, fault-tolerant consensus control schemes are designed to guarantee the bounded consensus of the closed-loop multiagent system with disturbed and faulty networks and actuator faults. The validity of the proposed adaptively distributed consensus control schemes is demonstrated by a multiagent system composed of five nonlinear forced pendulums.

Global exponential stability of discrete-time almost automorphic Caputo-Fabrizio BAM fuzzy neural networks via exponential Euler technique

Abstract: Exponential Euler discrete schemes have been widely employed in the studies of Caputo fractional order differential equations, but almost no literature concerns the Caputo–Fabrizio case. In current work, exponential discrete form has been set up to study Caputo–Fabrizio fuzzy BAM neural networks (CF-FBAMNNs). The research findings tell someone that (1) the exponential discrete form characterizes the continuous CF-FBAMNNs superior to the classical Euler discrete technique; (2) this type discrete technique pertains to the implicit Euler form, which can be calculated by PECE algorithm. Furthermore, the existence of a unique bounded asymptotically almost automorphic sequence solution and global exponential stability of the proposed discrete-time models are investigated. More importantly, the current works make up for the lacks in the existing literatures and build a set of new theories and methods in studying discrete-time Caputo–Fabrizio models in the fields of science and engineering.

Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis

Abstract: The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization. By the proposed controller, the hysteresis's effect can be compensated effectively and the tracking error can be bounded by an explicit function of design parameters. Simultaneously, all other signals in the closed-loop system can be remaining bounded. Finally, two examples are presented to verify the effectiveness of the proposed method.

Effects of bounded noise and time delay on signal transmission in excitable neural networks

Abstract: The influence of bounded noise and time delay on the sub–threshold signal transmission in FitzHugh–Nagumo neuronal networks is studied in this paper. It is found that the signal transmission performance can be enhanced by moderate noise levels in neuronal systems. There is an optimal cross–correlation time where the noise enhances signal transmission best. Moreover, the positively correlated bounded noise–induced stochastic anti–resonance phenomenon monotonically enhances with the increasing cross–correlated intensity. Interestingly, the stochastic anti–resonance phenomenon disappears when the noise is negatively correlated. With the fine–tuning of the time delay, the resonance peaks occur at each half–integer multiples of the signal period, implying delay–induced multiple stochastic resonances. The sub–harmonic stochastic resonance and stochastic anti–resonance alternatively appear at the appropriate coupling strength. These results may provide a novel perspective on sub–threshold signal transmission in the nervous system.

Event-Triggered ADP for Tracking Control of Partially Unknown Constrained Uncertain Systems

Abstract: An event-triggered adaptive dynamic programming (ADP) algorithm is developed in this article to solve the tracking control problem for partially unknown constrained uncertain systems. First, an augmented system is constructed, and the solution of the optimal tracking control problem of the uncertain system is transformed into an optimal regulation of the nominal augmented system with a discounted value function. The integral reinforcement learning is employed to avoid the requirement of augmented drift dynamics. Second, the event-triggered ADP is adopted for its implementation, where the learning of neural network weights not only relaxes the initial admissible control but also executes only when the predefined execution rule is violated. Third, the tracking error and the weight estimation error prove to be uniformly ultimately bounded, and the existence of a lower bound for the interexecution times is analyzed. Finally, simulation results demonstrate the effectiveness of the present event-triggered ADP method.