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This paper investigates the issue of delay-dependent stabilization for uncertain neural networks with two additive time-varying delay components. For one thing, the important time-varying information h1(t), h2(t) and h(t) has been taken full into account. On the other hand, we take full use of the cross terms of neuron activation functions and some effective integral inequalities. Finally, less conservative...
This paper investigates the problem of stability analysis of neural networks with time-varying delay. Some augmented double integral term and triple integral terms are introduced to the Lyapunov-Krasovskii functional (LKF). And the derivative of the LKF is estimated by the free-matrix-based integral inequality. Then, a less conservative stability criterion is derived. Finally, a numerical example...
In this paper, a new method to construct neural-network-based hysteresis model is proposed. At first, a new backlash operator is presented so as to construct a basic model. And then, based on the basic model, a two-dimension input space is established so that the one-to-multiple mapping of hysteresis can be transformed into a one-to-one mapping which can be identified by using traditional method of...
In this paper, an adaptive stable control method is proposed for the trajectory tracking of nonholonomic wheeled mobile robots with uncertainties. This control method is designed at the dynamical level. The kinematic constraints of the mobile robot are transformed into the chained equations, and the desired trajectory is assumed to meet the nonholonomic constraints. An adaptive control law is presented...
As a promising renewable alternative, a significant contribution to a photovoltaic power generation system in the future, but also brought new problems related to the integration of power quality, including voltage control and reactive power compensation. This study proposes a new adaptive neural network DC / AC converter-based control. In this study, the use of neural network control to increase...
In this paper, the problem of asymptotic stability criteria for neural networks with leakage time-varying delays is considered. By constructing appropriate Lyapunov- Krasovskii functional with double integral terms, introducing free-weighting matrices, new asymptotic stability criteria are derived to guarantee the stability of the delayed neural networks. The derived stability condition is dependent...
In this paper, the state estimation problem is investigated for a class of discrete-time stochastic neural networks with event-triggered transmission (ETT) mechanism. Different from the traditional periodic communication mechanism, the ETT mechanism employed in this paper possesses the advantage of mitigating the network traffic resulting from the unnecessary sending and receiving data between the...
This paper considers the effectiveness of neural-network-based reinforcement learning (RLNN) controller in discrete time for the underactuated ships. The path following problem of underactuated ships is solved by utilizing RLNN controller together with the Line-of-Sight (LOS) guidance law. The proper form of variables in RLNN controller compatible with the variables in ship maneuvering model is derived...
This paper aims to study delayed Hopfield neural networks with fractional-order Caputo derivative. With the help of the equivalent norm techniques and fixed point theorem, the new sufficient conditions to assure the existence, uniqueness and stability of fractional-order Hopfield neural networks with delays are obtained. Finally, the efficiency of the derived criteria is illustrated by two examples.
This paper considers the tracking fault-tolerant controller design problem for a class of nonlinear systems with unknown functions and actuator dead-zone. Based on dynamic surface control scheme and neural network approximated technique, some assumptions on nonlinear functions are removed. Also, the structure of controller is simple without the problem of ‘explosion of complexity’. Simultaneously,...
This paper investigates an adaptive distributed consensus tracking problem of uncertain nonlinear multi-agent systems in pure-feedback form on a directed graph. The unknown continuous nonlinear functions induced from the controller design procedure are approximated by Radial basis function neural networks (RBFNNs). Based on the distributed dynamic surface control technique, the problem of “explosion...
This paper focuses on the problem of exponential stability for neural networks with time-varying delay. By introducing a novel Lyapunov-Krasovskii functional, a new criterion of exponential stability is derived. This newly presented criterion does not require all the symmetric matrices involved in the employed integral terms of the quadratic Lyapunov-Krasovskii functional to be positive definite....
The synchronization of uncertain chaotic neural networks with time delays was studied in this paper. Based on the sliding mode control (SMC) approach, some sufficient conditions for synchronization of the two coupled networks are obtained. Finally, an example and its simulation are given to illustrate the effectiveness of our results.
In this paper, decentralized output feedback prescribed performance control problem is investigated for a class of large-scale time-delay systems with uncertain parameters and unmodeled dynamics. First, via a kind of state transformation, the construction of the system is changed. Then, by linear matrix inequality (LMI), the decentralized filters are designed. Further, combine with radial basis function...
In this paper, we investigate the cluster synchronization problem with unbounded time-varying delays for complex networks by adding some external controllers. Previous related works mainly focused on bounded time-varying or constant time delays, which may not be consistent with the real world. Therefore, unbounded time-varying delays is considered in this paper, which can be regarded as the main difference...
As a nonlinear and time-varying complex dynamic system, wastewater treatment process (WWTP) is difficult to be controlled. In this paper, to control the dissolved oxygen (DO) concentration in a WWTP, a growing and pruning recurrent fuzzy neural network (GPRFNN)-based control system is proposed which contains RFNN controllers and RFNN identifier. The identifier is used to model the WWTP with an adaptive...
For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances and model unknown nonlinearity affect the precision of trajectory tracking, A robust adaptive neural network dynamic surface control method is proposed. The ultra-low altitude airdrop longitudinal dynamics with actuator input nonlinearity is established, the neural network...
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