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In this paper, a neural network (NN)-based distributed cooperative identification strategy with event-triggered communication is studied for a group of coupled identical nonlinear systems. We develop a distributed cooperative learning law in the context of event-triggered communication, where an agent will transmit its NN weights to its neighbors only when its weight trigger error norm exceeds an exponentially decreasing threshold. It is proven that the estimated weights of all radial basis function NNs converge to a small neighborhood of their optimal values. Therefore, the unknown nonlinear function is approximated along the union of all the system trajectories. It is further proven that there exists a positive minimum interevent interval and Zeno behavior can be avoided. Finally, we give a simulation example to demonstrate these features. [ABSTRACT FROM AUTHOR]

In this paper, we study the design of a fast finite-time consensus protocol for multiple nonlinear mechanical systems with uncertainties and disturbance. Combining the theories of finite-time control and algebraic graph, a distributed adaptive fast finite-time consensus protocol is proposed in a recursive design way. In the designed control scheme, the unknown nonlinear functions are modelled by neural networks. With the help of fast finite-time boundedness lemma, the practical finite-time consensus of the multiple mechanical systems is proved. In the end, the validity of the proposed protocol is confirmed by a numerical example of multiple manipulators. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the problem of distributed containment control for pure‐feedback nonlinear multiagent systems under a directed graph topology is investigated. The dynamics of each agent are molded by high‐order nonaffine pure‐feedback form. Neural networks are employed to identify unknown nonlinear functions, and dynamic surface control technique is used to avoid the problem of explosion of complexity inherent in backstepping design procedure. The Frobenius norm of the ideal neural network weighting matrices is estimated, which is helpful to reduce the number of the adaptive tuning law and alleviate the networked communication burden. The proposed distributed containment controllers guarantee that all signals in the closed‐loop systems are cooperatively semiglobally uniformly ultimately bounded, and the outputs of followers are driven into a convex hull spanned by the multiple dynamic leaders. Finally, the effectiveness of the developed method is demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of adaptive fault tolerant control for a class of dynamic systems with unknown un-modeled actuator faults. The fault model is assumed to be an unknown nonlinear function of control input, not in the traditional form in which the faults can be described as gain and/or bias faults. Using the property of the basic function of neural networks and the implicit function theorem, a novel neural networks-based fault tolerant controller is designed. Finally, the lateral dynamics of a front-wheeled steered vehicle is used to demonstrate the efficiency of the proposed design techniques. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive neural network control approach is developed for a class of remotely operated vehicles whose velocity state and angular velocity state in the body-fixed frame are unmeasured. Unlike most previous control approaches, it doesn’t need thrust model and the thruster control signal is considered as the input of control system directly. Using local recurrent neural network to approximate the unknown nonlinear functions, an adaptive observer is introduced for state estimation. Under the framework of the backstepping design, adaptive neural network control law is constructed based on the output of local recurrent neural network and state estimation. The stability analysis is given by Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations. [ABSTRACT FROM AUTHOR]

An adaptive control scheme is studied for unified chaotic systems with unknown function and dead-zone input. Because uncertain nonlinear property is included in the considered unified chaotic systems, the neural networks are used to approximate the uncertainties. An adaptive technique is employed to construct the neural controllers and compensate for the dead-zone parameters. By using the scheme, the chaotic phenomena for unified chaotic systems are overcome. It is proven that the proposed algorithm can guarantee that all the signals in the closed-loop system are bounded and the system states can converge to a neighborhood of zero based on the Lyapunov analysis method. The simulation example for a unified chaotic system is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

SUMMARY Recently, an [ABSTRACT FROM AUTHOR]

A robust adaptive NN output feedback control is proposed to control a class of uncertain discrete-time nonlinear multi-input-multi-output (MIMO) systems. The high-order neural networks are utilized to approximate the unknown nonlinear functions in the systems. Compared with the previous research for discrete-time MIMO systems, robustness of the proposed adaptive algorithm is obvious improved. Using Lyapunov stability theorem, the results show all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of zero by choosing the design parameters appropriately. [ABSTRACT FROM AUTHOR]